Journal articles: 'Computational social networks'

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Nasution, Mahyuddin K. M., Rahmad Syah, and Marischa Elveny. "Social Network Analysis: Towards Complexity Problem." Webology 18, no. 2 (December 23, 2021): 449–61. http://dx.doi.org/10.14704/web/v18i2/web18332.

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Social network analysis is a advances from field of social networks. The structuring of social actors, with data models and involving intelligence abstracted in mathematics, and without analysis it will not present the function of social networks. However, graph theory inherits process and computational procedures for social network analysis, and it proves that social network analysis is mathematical and computational dependent on the degree of nodes in the graph or the degree of social actors in social networks. Of course, the process of acquiring social networks bequeathed the same complexity toward the social network analysis, where the approach has used the social network extraction and formulated its consequences in computing.

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Penn, A. "Synthetic networks — Spatial, social, structural and computational." BT Technology Journal 24, no. 3 (July 2006): 49–56. http://dx.doi.org/10.1007/s10550-006-0075-0.

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Wu, Jia, Fangfang Gou, Wangping Xiong, and Xian Zhou. "A Reputation Value-Based Task-Sharing Strategy in Opportunistic Complex Social Networks." Complexity 2021 (November 26, 2021): 1–16. http://dx.doi.org/10.1155/2021/8554351.

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As the Internet of Things (IoT) smart mobile devices explode in complex opportunistic social networks, the amount of data in complex networks is increasing. Large amounts of data cause high latency, high energy consumption, and low-reliability issues when dealing with computationally intensive and latency-sensitive emerging mobile applications. Therefore, we propose a task-sharing strategy that comprehensively considers delay, energy consumption, and terminal reputation value (DERV) for this context. The model consists of a task-sharing decision model that integrates latency and energy consumption, and a reputation value-based model for the allocation of the computational resource game. The two submodels apply an improved particle swarm algorithm and a Lagrange multiplier, respectively. Mobile nodes in the complex social network are given the opportunity to make decisions so that they can choose to share computationally intensive, latency-sensitive computing tasks to base stations with greater computing power in the same network. At the same time, to prevent malicious competition from end nodes, the base station decides the allocation of computing resources based on a database of reputation values provided by a trusted authority. The simulation results show that the proposed strategy can meet the service requirements of low delay, low power consumption, and high reliability for emerging intelligent applications. It effectively realizes the overall optimized allocation of computation sharing resources and promotes the stable transmission of massive data in complex networks.

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Nwanga, E. M., K. C. Okafor, G. A. Chukwudebe, and I. E. Achumba. "Computational Robotics: An Alternative Approach for Predicting Terrorist Networks." International Journal of Robotics and Automation Technology 8 (November 24, 2021): 1–11. http://dx.doi.org/10.31875/2409-9694.2021.08.1.

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Increasing terrorist activities globally have attracted the attention of many researchers, policy makers and security agencies towards counterterrorism. The clandestine nature of terrorist networks have made them difficult for detection. Existing works have failed to explore computational characterization to design an efficient threat-mining surveillance system. In this paper, a computationally-aware surveillance robot that auto-generates threat information, and transmit same to the cloud-analytics engine is developed. The system offers hidden intelligence to security agencies without any form of interception by terrorist elements. A miniaturized surveillance robot with Hidden Markov Model (MSRHMM) for terrorist computational dissection is then derived. Also, the computational framework for MERHMM is discussed while showing the adjacency matrix of terrorist network as a determinant factor for its operation. The model indicates that the terrorist network have a property of symmetric adjacency matrix while the social network have both asymmetric and symmetric adjacency matrix. Similarly, the characteristic determinant of adjacency matrix as an important operator for terrorist network is computed to be -1 while that of a symmetric and an asymmetric in social network is 0 and 1 respectively. In conclusion, it was observed that the unique properties of terrorist networks such as symmetric and idempotent property conferred a special protection for the terrorist network resilience. Computational robotics is shown to have the capability of utilizing the hidden intelligence in attack prediction of terrorist elements. This concept is expected to contribute in national security challenges, defense and military intelligence.

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Atdag, Samet, and Haluk O. Bingol. "Computational models for commercial advertisements in social networks." Physica A: Statistical Mechanics and its Applications 572 (June 2021): 125916. http://dx.doi.org/10.1016/j.physa.2021.125916.

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Ismaili, Anisse, and Patrice Perny. "Computational social choice for coordination in agent networks." Annals of Mathematics and Artificial Intelligence 77, no. 3-4 (June 13, 2015): 335–59. http://dx.doi.org/10.1007/s10472-015-9462-x.

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Tomassini, Marco, and Alberto Antonioni. "Computational Behavioral Models for Public Goods Games on Social Networks." Games 10, no. 3 (September 2, 2019): 35. http://dx.doi.org/10.3390/g10030035.

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Cooperation is a fundamental aspect of well-organized societies and public good games are a useful metaphor for modeling cooperative behavior in the presence of strong incentives to free ride. Usually, social agents interact to play a public good game through network structures. Here, we use social network structures and computational agent rules inspired by recent experimental work in order to develop models of agent behavior playing public goods games. The results of our numerical simulations based on a couple of simple models show that agents behave in a manner qualitatively similar to what has been observed experimentally. Computational models such as those presented here are very useful to interpret observed behavior and to enhance computationally the limited variation that is possible in the experimental domain. By assuming a priori reasonable individual behaviors, the easiness of running simulations could also facilitate exploration prior to any experimental work in order to vary and estimate a number of key parameters that would be very difficult, if not impossible, to change during the actual experiment.

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Li, Wei, and Sisi Zlatanova. "Significant Geo-Social Group Discovery over Location-Based Social Network." Sensors 21, no. 13 (July 2, 2021): 4551. http://dx.doi.org/10.3390/s21134551.

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Geo-social community detection over location-based social networks combining both location and social factors to generate useful computational results has attracted increasing interest from both industrial and academic communities. In this paper, we formulate a novel community model, termed geo-social group (GSG), to enforce both spatial and social factors to generate significant computational patterns and to investigate the problem of community detection over location-based social networks. Specifically, GSG detection aims to extract all group-venue clusters, where users are similar to each other in the same group and they are located in a minimum covering circle (MCC) for which the radius is no greater than a distance threshold γ. Then, we present a GSGD algorithm following a three-step paradigm to enumerate all qualified GSGs in a large network. We propose effective optimization techniques to efficiently enumerate all communities in a network. Furthermore, we extend a significant GSG detection problem to top-k geo-social group (TkGSG) mining. Rather than extracting all qualified GSGs in a network, TkGSG aims to return k feasibility groups to guarantee the diversity. We prove the hardness of computing the TkGSGs. Nevertheless, we propose the effective greedy approach with a guaranteed approximation ratio of 1−1/e. Extensive empirical studies on real and synthetic networks show the superiority of our algorithm when compared with existing methods and demonstrate the effectiveness of our new community model and the efficiency of our optimization techniques.

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Yan, Yeqing, Zhigang Chen, Jia Wu, and Leilei Wang. "An Effective Data Transmission Algorithm Based on Social Relationships in Opportunistic Mobile Social Networks." Algorithms 11, no. 8 (August 14, 2018): 125. http://dx.doi.org/10.3390/a11080125.

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With the popularization of mobile communication equipment, human activities have an increasing impact on the structure of networks, and so the social characteristics of opportunistic networks become increasingly obvious. Opportunistic networks are increasingly used in social situations. However, existing routing algorithms are not suitable for opportunistic social networks, because traditional opportunistic network routing does not consider participation in human activities, which usually causes a high ratio of transmission delay and routing overhead. Therefore, this research proposes an effective data transmission algorithm based on social relationships (ESR), which considers the community characteristics of opportunistic mobile social networks. This work uses the idea of the faction to divide the nodes in the network into communities, reduces the number of inefficient nodes in the community, and performs another contraction of the structure. Simulation results show that the ESR algorithm, through community transmission, is not only faster and safer, but also has lower transmission delay and routing overhead compared with the spray and wait algorithm, SCR algorithm and the EMIST algorithm.

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Wang, Pingshui, Jianwen Zhu, and Qinjuan Ma. "Private Data Protection in Social Networks Based on Blockchain." International Journal of Advanced Networking and Applications 14, no. 04 (2023): 5549–55. http://dx.doi.org/10.35444/ijana.2023.14407.

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With the rapid development of big data and social networks, user data in social networks are facing huge risks of privacy leakage. It is urgent to establish a complete and effective method for protecting private data in social networks. Based on the problem of information leakage in social networks, classifies user privacy data, and constructs different privacy data protection schemes through blockchain time stamp recording data storage, hash function anonymous operation of data, asymmetric encryption and digital signature of sending information. The blockchain-based privacy data protection method in social networks can effectively solve the privacy leakage problem in social networks, and provide a reference for the research in the field of information security and social network security. This paper designs a new blockchain-based privacy data protection scheme for different privacy disclosure categories, which provides a new solution to the current privacy disclosure problem in social networks. However, the existing methods will consume a lot of computational power in the process of information interaction. The subsequent research will optimize the computational power of blockchain and try to build a better blockchain social network privacy data protection system.

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Guzman, Grover E. C., Peter F. Stadler, and André Fujita. "Efficient Laplacian spectral density computations for networks with arbitrary degree distributions." Network Science 9, no. 3 (September 2021): 312–27. http://dx.doi.org/10.1017/nws.2021.10.

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AbstractThe network Laplacian spectral density calculation is critical in many fields, including physics, chemistry, statistics, and mathematics. It is highly computationally intensive, limiting the analysis to small networks. Therefore, we present two efficient alternatives: one based on the network’s edges and another on the degrees. The former gives the exact spectral density of locally tree-like networks but requires iterative edge-based message-passing equations. In contrast, the latter obtains an approximation of the spectral density using only the degree distribution. The computational complexities are 𝒪(|E|log(n)) and 𝒪(n), respectively, in contrast to 𝒪(n3) of the diagonalization method, where n is the number of vertices and |E| is the number of edges.

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Gilles, Robert, Tabitha James, Reza Barkhi, and Dimitrios Diamantaras. "Simulating Social Network Formation." International Journal of Virtual Communities and Social Networking 1, no. 4 (October 2009): 1–20. http://dx.doi.org/10.4018/jvcsn.2009092201.

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Social networks depict complex systems as graph theoretic models. The study of the formation of such systems (or networks) and the subsequent analysis of the network structures are of great interest. For information systems research and its impact on business practice, the ability to model and simulate a system of individuals interacting to achieve a certain socio-economic goal holds much promise for proper design and use of cyber networks. We use case-based decision theory to formulate a customizable model of information gathering in a social network. In this model, the agents in the network have limited awareness of the social network in which they operate and of the fixed, underlying payoff structure. Agents collect payoff information from neighbors within the prevailing social network, and they base their networking decisions on this information. Along with the introduction of the decision theoretic model, we developed software to simulate the formation of such networks in a customizable context to examine how the network structure can be influenced by the parameters that define social relationships. We present computational experiments that illustrate the growth and stability of the simulated social networks ensuing from the proposed model. The model and simulation illustrates how network structure influences agent behavior in a social network and how network structures, agent behavior, and agent decisions influence each other.

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Kumar Behera, Ranjan, Santanu Kumar Rath, Sanjay Misra, Robertas Damaševičius, and Rytis Maskeliūnas. "Distributed Centrality Analysis of Social Network Data Using MapReduce." Algorithms 12, no. 8 (August 9, 2019): 161. http://dx.doi.org/10.3390/a12080161.

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Analyzing the structure of a social network helps in gaining insights into interactions and relationships among users while revealing the patterns of their online behavior. Network centrality is a metric of importance of a network node in a network, which allows revealing the structural patterns and morphology of networks. We propose a distributed computing approach for the calculation of network centrality value for each user using the MapReduce approach in the Hadoop platform, which allows faster and more efficient computation as compared to the conventional implementation. A distributed approach is scalable and helps in efficient computations of large-scale datasets, such as social network data. The proposed approach improves the calculation performance of degree centrality by 39.8%, closeness centrality by 40.7% and eigenvalue centrality by 41.1% using a Twitter dataset.

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Hu, Hongzhi, Huajuan Mao, Xiaohua Hu, Feng Hu, Xuemin Sun, Zaiping Jing, and Yunsuo Duan. "Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation." Computational Intelligence and Neuroscience 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/181038.

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Due to the extensive social influence, public health emergency has attracted great attention in today’s society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event’s social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency.

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Ojugo, Arnold Adimabua, and Debby Oghenevwede Otakore. "Computational solution of networks versus cluster grouping for social network contact recommender system." International Journal of Informatics and Communication Technology (IJ-ICT) 9, no. 3 (December 1, 2020): 185. http://dx.doi.org/10.11591/ijict.v9i3.pp185-194.

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<span lang="EN-US">Graphs have become the dominant life-form of many tasks as they advance a structural system to represent many tasks and their corresponding relationships. A powerful role of networks and graphs is to bridge local feats that exist in vertices or nodal agents as they blossom into patterns that helps explain how nodes and their corresponding edges impacts a complex effect that ripple via a graph. User cluster are formed as a result of interactions between entities – such that many users today, hardly categorize their contacts into groups such as “family”, “friends”, “colleagues”. The need to analyze such user social graph via implicit clusters, enables the dynamism in contact management. Study seeks to implement this dynamism via a comparative study of the deep neural network and friend suggest algorithm. We analyze a user’s implicit social graph and seek to automatically create custom contact groups using metrics that classify such contacts based on a user’s affinity to contacts. Experimental results demonstrate the importance of both the implicit group relationships and the interaction-based affinity in suggesting friends.</span>

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Hicks, Jacqueline, Vincent A. Traag, and Ridho Reinanda. "Turning Digitised Newspapers into Networks of Political Elites." Asian Journal of Social Science 43, no. 5 (2015): 567–87. http://dx.doi.org/10.1163/15685314-04305004.

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This paper introduces the Elite Network Shifts (ENS) project to the Asian Studies community where computational techniques are used with digitised newspaper articles to describe changes in relations among Indonesian political elites. Reflecting on how “political elites” and “political relations” are understood by the elites, as well as across the disciplinary boundaries of the social and computational sciences, it suggests ways to operationalise these concepts for digital research. It then presents the results of a field trip where six Indonesian political elites were asked to evaluate the accuracy of their own computational networks generated by the project. The main findings of the paper are: (1) The computational identification of political elites is relatively successful, while much work remains on categorising their relations, (2) social scientists should focus on capturing single dimensions of complex social phenomena when using computational techniques, and (3) computational techniques are not able to capture multiple understandings of social concepts.

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Mensah, Dennis Nii Ayeh, Hui Gao, and Liang Wei Yang. "Approximation Algorithm for Shortest Path in Large Social Networks." Algorithms 13, no. 2 (February 6, 2020): 36. http://dx.doi.org/10.3390/a13020036.

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Proposed algorithms for calculating the shortest paths such as Dijikstra and Flowd-Warshall’s algorithms are limited to small networks due to computational complexity and cost. We propose an efficient and a more accurate approximation algorithm that is applicable to large scale networks. Our algorithm iteratively constructs levels of hierarchical networks by a node condensing procedure to construct hierarchical graphs until threshold. The shortest paths between nodes in the original network are approximated by considering their corresponding shortest paths in the highest hierarchy. Experiments on real life data show that our algorithm records high efficiency and accuracy compared with other algorithms.

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Lin, Mingkai, Wenzhong Li, Lynda J. Song, Cam-Tu Nguyen, Xiaoliang Wang, and Sanglu Lu. "SAKE." ACM Transactions on Knowledge Discovery from Data 15, no. 4 (June 2021): 1–21. http://dx.doi.org/10.1145/3441646.

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Katz centrality is a fundamental concept to measure the influence of a vertex in a social network. However, existing approaches to calculating Katz centrality in a large-scale network are unpractical and computationally expensive. In this article, we propose a novel method to estimate Katz centrality based on graph sampling techniques, which object to achieve comparable estimation accuracy of the state-of-the-arts with much lower computational complexity. Specifically, we develop a Horvitz–Thompson estimate for Katz centrality by using a multi-round sampling approach and deriving an unbiased mean value estimator. We further propose SAKE , a S ampling-based A lgorithm for fast K atz centrality E stimation. We prove that the estimator calculated by SAKE is probabilistically guaranteed to be within an additive error from the exact value. Extensive evaluation experiments based on four real-world networks show that the proposed algorithm can estimate Katz centralities for partial vertices with low sampling rate, low computation time, and it works well in identifying high influence vertices in social networks.

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Muthukrishna, Michael, and Mark Schaller. "Are Collectivistic Cultures More Prone to Rapid Transformation? Computational Models of Cross-Cultural Differences, Social Network Structure, Dynamic Social Influence, and Cultural Change." Personality and Social Psychology Review 24, no. 2 (June 28, 2019): 103–20. http://dx.doi.org/10.1177/1088868319855783.

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Societies differ in susceptibility to social influence and in the social network structure through which individuals influence each other. What implications might these cultural differences have for changes in cultural norms over time? Using parameters informed by empirical evidence, we computationally modeled these cross-cultural differences to predict two forms of cultural change: consolidation of opinion majorities into stronger majorities, and the spread of initially unpopular beliefs. Results obtained from more than 300,000 computer simulations showed that in populations characterized by greater susceptibility to social influence, there was more rapid consolidation of majority opinion and also more successful spread of initially unpopular beliefs. Initially unpopular beliefs also spread more readily in populations characterized by less densely connected social networks. These computational outputs highlight the value of computational modeling methods as a means to specify hypotheses about specific ways in which cross-cultural differences may have long-term consequences for cultural stability and cultural change.

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Chen, Jinjun, and Jianxun Liu. "Introduction: Social Computing and Social Networks." Journal of Organizational Computing and Electronic Commerce 24, no. 2-3 (April 3, 2014): 119–21. http://dx.doi.org/10.1080/10919392.2014.896712.

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Grandi, Umberto, Lawqueen Kanesh, Grzegorz Lisowski, Ramanujan Sridharan, and Paolo Turrini. "Identifying and Eliminating Majority Illusion in Social Networks." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (June 26, 2023): 5062–69. http://dx.doi.org/10.1609/aaai.v37i4.25634.

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Majority illusion occurs in a social network when the majority of the network vertices belong to a certain type but the majority of each vertex's neighbours belong to a different type, therefore creating the wrong perception, i.e., the illusion, that the majority type is different from the actual one. From a system engineering point of view, this motivates the search for algorithms to detect and, where possible, correct this undesirable phenomenon. In this paper we initiate the computational study of majority illusion in social networks, providing NP-hardness and parametrised complexity results for its occurrence and elimination.

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TSOUMANIS, A. C., C. I. SIETTOS, G. V. BAFAS, and I. G. KEVREKIDIS. "EQUATION-FREE MULTISCALE COMPUTATIONS IN SOCIAL NETWORKS: FROM AGENT-BASED MODELING TO COARSE-GRAINED STABILITY AND BIFURCATION ANALYSIS." International Journal of Bifurcation and Chaos 20, no. 11 (November 2010): 3673–88. http://dx.doi.org/10.1142/s0218127410027945.

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We focus on the "trijunction" between multiscale computations, bifurcation theory and social networks. In particular, we address how the Equation-Free approach, a recently developed computational framework, can be exploited to systematically extract coarse-grained, emergent dynamical information by bridging detailed, agent-based models of social interactions on networks, with macroscopic, systems-level, continuum numerical analysis tools. For our illustrations, we use a simple dynamic agent-based model describing the propagation of information between individuals interacting under mimesis in a social network with private and public information. We describe the rules governing the evolution of the agents' emotional state dynamics and discover, through simulation, multiple stable stationary states as a function of the network topology. Using the Equation-Free approach we track the dependence of these stationary solutions on network parameters and quantify their stability in the form of coarse-grained bifurcation diagrams.

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Niccolai, Alessandro, Francesco Grimaccia, Marco Mussetta, and Riccardo Zich. "Optimal Task Allocation in Wireless Sensor Networks by Means of Social Network Optimization." Mathematics 7, no. 4 (March 28, 2019): 315. http://dx.doi.org/10.3390/math7040315.

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Wireless Sensor Networks (WSN) have been widely adopted for years, but their role is growing significantly currently with the increase of the importance of the Internet of Things paradigm. Moreover, since the computational capability of small-sized devices is also increasing, WSN are now capable of performing relevant operations. An optimal scheduling of these in-network processes can affect both the total computational time and the energy requirements. Evolutionary optimization techniques can address this problem successfully due to their capability to manage non-linear problems with many design variables. In this paper, an evolutionary algorithm recently developed, named Social Network Optimization (SNO), has been applied to the problem of task allocation in a WSN. The optimization results on two test cases have been analyzed: in the first one, no energy constraints have been added to the optimization, while in the second one, a minimum number of life cycles is imposed.

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Raghavan, S., and Rui Zhang. "Influence Maximization with Latency Requirements on Social Networks." INFORMS Journal on Computing 34, no. 2 (March 2022): 710–28. http://dx.doi.org/10.1287/ijoc.2021.1095.

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Targeted marketing strategies are of significant interest in the smartapp economy. Typically, one seeks to identify individuals to strategically target in a social network so that the network is influenced at a minimal cost. In many practical settings, the effects of direct influence predominate, leading to the positive influence dominating set with partial payments (PIDS-PP) problem that we discuss in this paper. The PIDS-PP problem is NP-complete because it generalizes the dominating set problem. We discuss several mixed integer programming formulations for the PIDS-PP problem. First, we describe two compact formulations on the payment space. We then develop a stronger compact extended formulation. We show that when the underlying graph is a tree, this compact extended formulation provides integral solutions for the node selection variables. In conjunction, we describe a polynomial-time dynamic programming algorithm for the PIDS-PP problem on trees. We project the compact extended formulation onto the payment space, providing an equivalently strong formulation that has exponentially many constraints. We present a polynomial time algorithm to solve the associated separation problem. Our computational experience on a test bed of 100 real-world graph instances (with up to approximately 465,000 nodes and 835,000 edges) demonstrates the efficacy of our strongest payment space formulation. It finds solutions that are on average 0.4% from optimality and solves 80 of the 100 instances to optimality. Summary of Contribution: The study of influence propagation is important in a number of applications including marketing, epidemiology, and healthcare. Typically, in these problems, one seeks to identify individuals to strategically target in a social network so that the entire network is influenced at a minimal cost. With the ease of tracking consumers in the smartapp economy, the scope and nature of these problems have become larger. Consequently, there is considerable interest across multiple research communities in computationally solving large-scale influence maximization problems, which thus represent significant opportunities for the development of operations research–based methods and analysis in this interface. This paper introduces the positive influence dominating set with partial payments (PIDS-PP) problem, an influence maximization problem where the effects of direct influence predominate, and it is possible to make partial payments to nodes that are not targeted. The paper focuses on model development to solve large-scale PIDS-PP problems. To this end, starting from an initial base optimization model, it uses several operations research model strengthening techniques to develop two equivalent models that have strong computational performance (and can be theoretically shown to be the best model for trees). Computational experiments on a test bed of 100 real-world graph instances (with up to approximately 465,000 nodes and 835,000 edges) attest to the efficacy of the best model, which finds solutions that are on average 0.4% from optimality and solves 80 of the 100 instances to optimality.

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CONTE, ROSARIA, MARIO PAOLUCCI, and JORDI SABATER-MIR. "REPUTATION FOR INNOVATING SOCIAL NETWORKS." Advances in Complex Systems 11, no. 02 (April 2008): 303–20. http://dx.doi.org/10.1142/s0219525908001647.

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Reputation is a fundamental instrument of partner selection. Developed within the domain of electronic auctions, reputation technology is being been imported by other applications, from social networks to institutional evaluation. Its impact on trust enforcement is uncontroversial and its management is of primary concern for entrepreneurs and other economic operators. In this paper, we will briefly report on simulation-based studies of the role of reputation as a more tolerant form of social capital than familiarity networks. Whereas the latter exclude nontrustworthy partners, reputation is a more inclusive mechanism on which larger and more dynamic networks are constructed. After the presentation of the theory of reputation developed by the authors in the last decade, a computational system (REPAGE) for forming and exchanging reputation information will be presented and findings from experimental simulations recently run on this system will be resumed. Final remarks and ideas for future work will conclude the paper.

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Kashima, Yoshihisa, Andrew Perfors, Vanessa Ferdinand, and Elle Pattenden. "Ideology, communication and polarization." Philosophical Transactions of the Royal Society B: Biological Sciences 376, no. 1822 (February 22, 2021): 20200133. http://dx.doi.org/10.1098/rstb.2020.0133.

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Ideologically committed minds form the basis of political polarization, but ideologically guided communication can further entrench and exacerbate polarization depending on the structures of ideologies and social network dynamics on which cognition and communication operate. Combining a well-established connectionist model of cognition and a well-validated computational model of social influence dynamics on social networks, we develop a new model of ideological cognition and communication on dynamic social networks and explore its implications for ideological political discourse. In particular, we explicitly model ideologically filtered interpretation of social information, ideological commitment to initial opinion, and communication on dynamically evolving social networks, and examine how these factors combine to generate ideologically divergent and polarized political discourse. The results show that ideological interpretation and commitment tend towards polarized discourse. Nonetheless, communication and social network dynamics accelerate and amplify polarization. Furthermore, when agents sever social ties with those that disagree with them (i.e. structure their social networks by homophily), even non-ideological agents may form an echo chamber and form a cluster of opinions that resemble an ideological group. This article is part of the theme issue ‘The political brain: neurocognitive and computational mechanisms’.

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Tejaswi, V., P. V. Bindu, and P. Santhi Thilagam. "Influence maximisation in social networks." International Journal of Computational Science and Engineering 18, no. 2 (2019): 103. http://dx.doi.org/10.1504/ijcse.2019.097955.

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Thilagam, P. Santhi, V. Tejaswi, and P. V. Bindu. "Influence maximisation in social networks." International Journal of Computational Science and Engineering 18, no. 2 (2019): 103. http://dx.doi.org/10.1504/ijcse.2019.10019162.

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Dabkowski, Matthew F., Neng Fan, and Ronald Breiger. "Finding globally optimal macrostructure in multiple relation, mixed-mode social networks." Methodological Innovations 13, no. 3 (September 2020): 205979912096169. http://dx.doi.org/10.1177/2059799120961693.

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From the outset, computational sociologists have stressed leveraging multiple relations when blockmodeling social networks. Despite this emphasis, the majority of published research over the past 40 years has focused on solving blockmodels for a single relation. When multiple relations exist, a reductionist approach is often employed, where the relations are stacked or aggregated into a single matrix, allowing the researcher to apply single relation, often heuristic, blockmodeling techniques. Accordingly, in this article, we develop an exact procedure for the exploratory blockmodeling of multiple relation, mixed-mode networks. In particular, given (a) [Formula: see text] actors, (b) [Formula: see text] events, (c) an [Formula: see text] binary one-mode network depicting the ties between actors, and (d) an [Formula: see text] binary two-mode network representing the ties between actors and events, we use integer programming to find globally optimal [Formula: see text] image matrices and partitions, where [Formula: see text] and [Formula: see text] represent the number of actor and event positions, respectively. Given the problem’s computational complexity, we also develop an algorithm to generate a minimal set of non-isomorphic image matrices, as well as a complementary, easily accessible heuristic using the network analysis software Pajek. We illustrate these concepts using a simple, hypothetical example, and we apply our techniques to a terrorist network.

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Abuali, Khadija M., Liyth Nissirat, and Aida Al-Samawi. "Intrusion Detection Techniques in Social Media Cloud: Review and Future Directions." Wireless Communications and Mobile Computing 2023 (April 26, 2023): 1–25. http://dx.doi.org/10.1155/2023/6687023.

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As social media use increases, the number of users has risen also. This has increased the volume of data carried over the network, making it more important to secure users’ data and privacy from threats. As users are unaware of hackers, social media’s security flaws and new forms of attack will persist. Intrusion detection systems, therefore, are vital to identifying intrusion risks. This paper examines a variety of intrusion detection techniques used to detect cyberattacks on social media networks. The paper provides a summary of the prevalent attacks on social media networks, such as phishing, fake profiles, account compromise, and cyberbullying. Then, the most prevalent techniques for classifying network traffic, including statistical and artificial intelligence (AI) techniques, are addressed. The literature also demonstrates that because AI can manage vast, scalable networks, AI-based IDSs are more effective at classifying network traffic and detecting intrusions in complex social media networks. However, AI-based IDSs exhibit high computational and space complexities; therefore, despite their remarkable performance, they are more suitable for high computing power systems. Hybrid IDSs, utilizing statistical feature selection and shallow neural networks, may provide a compromise between computational requirements and efficiency. This investigation shows that accuracies of statistical techniques range from 90% to 97.5%. In contrast, AI and ML technique detection accuracy ranges from 78% to 99.95%. Similarly, swarm and evolutionary techniques achieved from 84% to 99.95% and deep learning-based detection techniques achieved from 45% to more than 99% detection rates. Convolutional neural network deep learning systems outperformed other methods due to their ability to automatically craft the features that would classify the network traffic with high accuracy.

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Taherdoost, Hamed. "Enhancing Social Media Platforms with Machine Learning Algorithms and Neural Networks." Algorithms 16, no. 6 (May 29, 2023): 271. http://dx.doi.org/10.3390/a16060271.

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Network analysis aids management in reducing overall expenditures and maintenance workload. Social media platforms frequently use neural networks to suggest material that corresponds with user preferences. Machine learning is one of many methods for social network analysis. Machine learning algorithms operate on a collection of observable features that are taken from user data. Machine learning and neural network-based systems represent a topic of study that spans several fields. Computers can now recognize the emotions behind particular content uploaded by users to social media networks thanks to machine learning. This study examines research on machine learning and neural networks, with an emphasis on social analysis in the context of the current literature.

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Faresi, Ahmed Al, Ahmed Alazzawe, and Anis Alazzawe. "PRIVACY LEAKAGE IN HEALTH SOCIAL NETWORKS." Computational Intelligence 30, no. 3 (March 21, 2013): 514–34. http://dx.doi.org/10.1111/coin.12005.

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Mao, Fubing, Lijia Ma, Qiang He, and Gaoxi Xiao. "Match making in complex social networks." Applied Mathematics and Computation 371 (April 2020): 124928. http://dx.doi.org/10.1016/j.amc.2019.124928.

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Raguru, Jaya Krishna, and Devi Prasad Sharma. "Heterogeneous Influence Maximization Through Community Detection in Social Networks." International Journal of Ambient Computing and Intelligence 12, no. 4 (October 2021): 118–31. http://dx.doi.org/10.4018/ijaci.2021100107.

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The problem of identifying a seed set composed of K nodes that increase influence spread over a social network is known as influence maximization (IM). Past works showed this problem to be NP-hard and an optimal solution to this problem using greedy algorithms achieved only 63% of spread. However, this approach is expensive and suffered from performance issues like high computational cost. Furthermore, in a network with communities, IM spread is not always certain. In this paper, heterogeneous influence maximization through community detection (HIMCD) algorithm is proposed. This approach addresses initial seed nodes selection in communities using various centrality measures, and these seed nodes act as sources for influence spread. A parallel influence maximization is applied with the aid of seed node set contained in each group. In this approach, graph is partitioned and IM computations are done in a distributed manner. Extensive experiments with two real-world datasets reveals that HCDIM achieves substantial performance improvement over state-of-the-art techniques.

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Soelistijanto, Bambang, and Vittalis Ayu. "Improving Traffic Load Distribution Fairness in Mobile Social Networks." Algorithms 15, no. 7 (June 22, 2022): 222. http://dx.doi.org/10.3390/a15070222.

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Mobile social networks suffer from an unbalanced traffic load distribution due to the heterogeneity in mobility of nodes (humans) in the network. A few nodes in these networks are highly mobile, and the proposed social-based routing algorithms are likely to choose these most “social” nodes as the best message relays. Finally, this could lead to inequitable traffic load distribution and resource utilisation, such as faster battery drain and/or storage consumption of the most (socially) popular nodes. We propose a framework called Traffic Load Distribution Aware (TraLDA) to improve traffic load balancing across network nodes. We present a novel method for calculating node popularity which takes into account both node inherent and social-relations popularity. The former is purely determined by the node’s sociability level in the network, and in TraLDA is computed using the Kalman prediction which considers the node’s periodicity behaviour. However, the latter takes the benefit of interactions with more popular neighbours (acquaintances) to boost the popularity of lower (social) level nodes. Using extensive simulations in the Opportunistic Network Environment (ONE) driven by real human mobility scenarios, we show that our proposed strategy enhances the traffic load distribution fairness of the classical, yet popular social-aware routing algorithms BubbleRap and SimBet without negatively impacting the overall delivery performance.

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Moeinifar, V., and S. Gündüç. "Zealots' effect on opinion dynamics in complex networks." Mathematical Modeling and Computing 8, no. 2 (2021): 203–14. http://dx.doi.org/10.23939/mmc2021.02.203.

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In this paper, we study zealots' effects on social networks. Our social network is based on scale-free networks using Barabasi–Albert method and random networks using Erdős–Rényi method. We used a pre-studied modified Voter model that includes zealots, individuals who never change their opinions. We chose prominent individuals (i.e. hubs) as zealots. In this way we first chose important individuals with high degree (hubs); second, individuals with high closeness. And then examined the consensus time compared with that zealots are chosen as non-important individuals. We found that the time to get to the consensus state in social networks is the same for different numbers of zealots but with the same degrees of contamination with zealotry. For example, one zealot's effect with a degree of 64 is same to 8 zealots' effects with a degree of 8.

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Sun, Qing, and Zhong Yao. "Evolutionary Game Analysis of Competitive Information Dissemination on Social Networks: An Agent-Based Computational Approach." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/679726.

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Social networks are formed by individuals, in which personalities, utility functions, and interaction rules are made as close to reality as possible. Taking the competitive product-related information as a case, we proposed a game-theoretic model for competitive information dissemination in social networks. The model is presented to explain how human factors impact competitive information dissemination which is described as the dynamic of a coordination game and players’ payoff is defined by a utility function. Then we design a computational system that integrates the agent, the evolutionary game, and the social network. The approach can help to visualize the evolution of % of competitive information adoption and diffusion, grasp the dynamic evolution features in information adoption game over time, and explore microlevel interactions among users in different network structure under various scenarios. We discuss several scenarios to analyze the influence of several factors on the dissemination of competitive information, ranging from personality of individuals to structure of networks.

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Michalak, T. P., K. V. Aadithya, P. L. Szczepanski, B. Ravindran, and N. R. Jennings. "Efficient Computation of the Shapley Value for Game-Theoretic Network Centrality." Journal of Artificial Intelligence Research 46 (April 20, 2013): 607–50. http://dx.doi.org/10.1613/jair.3806.

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The Shapley value---probably the most important normative payoff division scheme in coalitional games---has recently been advocated as a useful measure of centrality in networks. However, although this approach has a variety of real-world applications (including social and organisational networks, biological networks and communication networks), its computational properties have not been widely studied. To date, the only practicable approach to compute Shapley value-based centrality has been via Monte Carlo simulations which are computationally expensive and not guaranteed to give an exact answer. Against this background, this paper presents the first study of the computational aspects of the Shapley value for network centralities. Specifically, we develop exact analytical formulae for Shapley value-based centrality in both weighted and unweighted networks and develop efficient (polynomial time) and exact algorithms based on them. We empirically evaluate these algorithms on two real-life examples (an infrastructure network representing the topology of the Western States Power Grid and a collaboration network from the field of astrophysics) and demonstrate that they deliver significant speedups over the Monte Carlo approach. For instance, in the case of unweighted networks our algorithms are able to return the exact solution about 1600 times faster than the Monte Carlo approximation, even if we allow for a generous 10% error margin for the latter method.

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Bonato, Anthony, Noor Hadi, Paul Horn, Paweł Prałat, and Changping Wang. "Models of Online Social Networks." Internet Mathematics 6, no. 3 (January 2009): 285–313. http://dx.doi.org/10.1080/15427951.2009.10390642.

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Nguyen, Minh, Mehmet Aktas, and Esra Akbas. "Bot Detection on Social Networks Using Persistent Homology." Mathematical and Computational Applications 25, no. 3 (September 4, 2020): 58. http://dx.doi.org/10.3390/mca25030058.

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The growth of social media in recent years has contributed to an ever-increasing network of user data in every aspect of life. This volume of generated data is becoming a vital asset for the growth of companies and organizations as a powerful tool to gain insights and make crucial decisions. However, data is not always reliable, since primarily, it can be manipulated and disseminated from unreliable sources. In the field of social network analysis, this problem can be tackled by implementing machine learning models that can learn to classify between humans and bots, which are mostly harmful computer programs exploited to shape public opinions and circulate false information on social media. In this paper, we propose a novel topological feature extraction method for bot detection on social networks. We first create weighted ego networks of each user. We then encode the higher-order topological features of ego networks using persistent homology. Finally, we use these extracted features to train a machine learning model and use that model to classify users as bot vs. human. Our experimental results suggest that using the higher-order topological features coming from persistent homology is promising in bot detection and more effective than using classical graph-theoretic structural features.

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Wang, Haibo, Bahram Alidaee, Wei Wang, and Wei Ning. "Critical Infrastructure Management for Telecommunication Networks." International Journal of Knowledge and Systems Science 5, no. 1 (January 2014): 1–13. http://dx.doi.org/10.4018/ijkss.2014010101.

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Telecommunication network infrastructures both stationary and ad hoc, play an important role in maintaining the stability of society worldwide. The protection of these critical infrastructures and their supporting structures become highly challenged due to its complexity. The understanding of interdependency of these infrastructures is the essential step to protect these infrastructures from destruction and attacks. This paper presents a critical infrastructure detection model to discover the interdependency based on the theories from social networks and new telecommunication pathways while this study transforms social theory into computational constructions. The procedure and solution of protecting critical infrastructures are discussed and computational results from the proposed model are presented.

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Shi, Zhenyu, Wei Wei, Matjaž Perc, Baifeng Li, and Zhiming Zheng. "Coupling group selection and network reciprocity in social dilemmas through multilayer networks." Applied Mathematics and Computation 418 (April 2022): 126835. http://dx.doi.org/10.1016/j.amc.2021.126835.

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Zhang, Xiaoxian, Jianpei Zhang, and Jing Yang. "Large-scale dynamic social data representation for structure feature learning." Journal of Intelligent & Fuzzy Systems 39, no. 4 (October 21, 2020): 5253–62. http://dx.doi.org/10.3233/jifs-189010.

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The problems caused by network dimension disasters and computational complexity have become an important issue to be solved in the field of social network research. The existing methods for network feature learning are mostly based on static and small-scale assumptions, and there is no modified learning for the unique attributes of social networks. Therefore, existing learning methods cannot adapt to the dynamic and large-scale of current social networks. Even super large scale and other features. This paper mainly studies the feature representation learning of large-scale dynamic social network structure. In this paper, the positive and negative damping sampling of network nodes in different classes is carried out, and the dynamic feature learning method for newly added nodes is constructed, which makes the model feasible for the extraction of structural features of large-scale social networks in the process of dynamic change. The obtained node feature representation has better dynamic robustness. By selecting the real datasets of three large-scale dynamic social networks and the experiments of dynamic link prediction in social networks, it is found that DNPS has achieved a large performance improvement over the benchmark model in terms of prediction accuracy and time efficiency. When the α value is around 0.7, the model effect is optimal.

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Muniesa, Fabian, and Ivan Tchalakov. "Networks, Agents and Models." International Journal of Actor-Network Theory and Technological Innovation 4, no. 1 (January 2012): 13–23. http://dx.doi.org/10.4018/jantti.2012010102.

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Actor-Network Theory proves particularly inspiring in reconsidering the tenets of quantitative research and computational methods in the social sciences. However, translating insights from this perspective into operational models is problematic. The paper examines, in the form of a dialogue, critical problems of the computational modelling of network topologies considered from the point of view of Actor-Network Theory. In particular, the paper discusses the impetus of simulation and the inappropriateness of the distinction between agents and links.

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Et.al, Mahyuddin K. M. Nasution. "Social Network Extraction Unsupervised." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 4443–49. http://dx.doi.org/10.17762/turcomat.v12i3.1824.

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In the era of information technology, the two developing sides are data science and artificial intelligence. In terms of scientific data, one of the tasks is the extraction of social networks from information sources that have the nature of big data. Meanwhile, in terms of artificial intelligence, the presence of contradictory methods has an impact on knowledge. This article describes an unsupervised as a stream of methods for extracting social networks from information sources. There are a variety of possible approaches and strategies to superficial methods as a starting concept. Each method has its advantages, but in general, it contributes to the integration of each other, namely simplifying, enriching, and emphasizing the results.

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Choi, Minje, David Jurgens, and Daniel M. Romero. "Analyzing the Engagement of Social Relationships during Life Event Shocks in Social Media." Proceedings of the International AAAI Conference on Web and Social Media 17 (June 2, 2023): 149–60. http://dx.doi.org/10.1609/icwsm.v17i1.22134.

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Individuals experiencing unexpected distressing events, shocks, often rely on their social network for support. While prior work has shown how social networks respond to shocks, these studies usually treat all ties equally, despite differences in the support provided by different social relationships. Here, we conduct a computational analysis on Twitter that examines how responses to online shocks differ by the relationship type of a user dyad. We introduce a new dataset of over 13K instances of individuals' self-reporting shock events on Twitter and construct networks of relationship-labeled dyadic interactions around these events. By examining behaviors across 110K replies to shocked users in a pseudo-causal analysis, we demonstrate relationship-specific patterns in response levels and topic shifts. We also show that while well-established social dimensions of closeness such as tie strength and structural embeddedness contribute to shock responsiveness, the degree of impact is highly dependent on relationship and shock types. Our findings indicate that social relationships contain highly distinctive characteristics in network interactions, and that relationship-specific behaviors in online shock responses are unique from those of offline settings.

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Zhang, Yichuan, Yibo Yong, Shujun Yang, and Tian Zhang. "A New Discrete Grid-Based Bacterial Foraging Optimizer to Solve Complex Influence Maximization of Social Networks." Discrete Dynamics in Nature and Society 2021 (October 22, 2021): 1–13. http://dx.doi.org/10.1155/2021/3101042.

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Influence maximization (IM) is fundamental to social network applications. It aims to find multiple seed nodes with an enormous impact cascade to maximize these nodes’ spread of influence in social networks. Traditional methods for solving influence maximization of the social network, such as the distance method, greedy method, and PageRank method, may suffer from issues of low calculation accuracy and high computational cost. In this paper, we propose a new bacterial foraging optimization algorithm to solve the IM problem based on the complete-three-layer-influence (CTLI) evaluation model. In this algorithm, a novel grid-based reproduction strategy and a direction-adjustment-based chemotaxis strategy are devised to enhance the algorithm’s searchability. Finally, we conduct comprehensive experiments on four social network cases to verify the effectiveness of the proposed algorithm. The experimental results show that our proposed algorithm effectively solves the social network’s influence maximization.

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Castro, Luis E., and Nazrul I. Shaikh. "Influence Estimation and Opinion-Tracking Over Online Social Networks." International Journal of Business Analytics 5, no. 4 (October 2018): 24–42. http://dx.doi.org/10.4018/ijban.2018100102.

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This article presents a restricted maximum likelihood-based algorithm to estimate who influences whose opinions and to what degree when agents share their opinions over large online social networks such as Twitter. The proposed algorithm uses multi-core processing and distributed computing to provide a scalable solution as the optimization problems are large in scale; a network with 10,000 agents and average connectivity of 100 requires estimates of about 1 million parameters. A computational study is then used to show that the estimates are efficient and robust when the full rank conditions for the covariance matrix are met. The results also highlight the importance of the quantity of the information being shared over the social network for the inference of the influence structure.

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Wang, Fang, and Yaoru Sun. "SELF-ORGANIZING PEER-TO-PEER SOCIAL NETWORKS." Computational Intelligence 24, no. 3 (August 2008): 213–33. http://dx.doi.org/10.1111/j.1467-8640.2008.00328.x.

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Perova, Juю P., V. P. Grigoriev, and D. O. Zhukov. "Models and methods for analyzing complex networks and social network structures." Russian Technological Journal 11, no. 2 (April 7, 2023): 33–49. http://dx.doi.org/10.32362/2500-316x-2023-11-2-33-49.

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Objectives. The study aimed to investigate contemporary models, methods, and tools used for analyzing complex social network structures, both on the basis of ready-made solutions in the form of services and software, as well as proprietary applications developed using the Python programming language. Such studies make it possible not only to predict the dynamics of social processes (changes in social attitudes), but also to identify trends in socioeconomic development by monitoring users’ opinions on important economic and social issues, both at the level of individual territorial entities (for example, districts, settlements of small towns, etc.) and wider regions.Methods. Dynamic models and stochastic dynamics analysis methods, which take into account the possibility of self-organization and the presence of memory, are used along with user deanonymization methods and recommendation systems, as well as statistical methods for analyzing profiles in social networks. Numerical modeling methods for analyzing complex networks and processes occurring in them are considered and described in detail. Special attention is paid to data processing in complex network structures using the Python language and its various available libraries.Results. The specifics of the tasks to be solved in the study of complex network structures and their interdisciplinarity associated with the use of methods of system analysis are described in terms of the theory of complex networks, text analytics, and computational linguistics. In particular, the dynamic models of processes observed in complex social network systems, as well as the structural characteristics of such networks and their relationship with the observed dynamic processes including using the theory of constructing dynamic graphs are studied. The use of neural networks to predict the evolution of dynamic processes and structure of complex social systems is investigated. When creating models describing the observed processes, attention is focused on the use of computational linguistics methods to extract knowledge from text messages of users of social networks.Conclusions. Network analysis can be used to structure models of interaction between social units: people, collectives, organizations, etc. Compared with other methods, the network approach has the undeniable advantage of operating with data at different levels of research to ensure its continuity. Since communication in social networks almost entirely consists of text messages and various publications, almost all relevant studies use textual analysis methods in conjunction with machine learning and artificial intelligence technologies. Of these, convolutional neural networks demonstrated the best results. However, the use of support vector and decision tree methods should also be mentioned, since these contributed considerably to accuracy. In addition, statistical methods are used to compile data samples and analyze obtained results.

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Journal articles on the topic 'Computational social networks'

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