Journal articles: 'SPREADER NODES'

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Sheikhahmadi, Amir, and Mohammad Ali Nematbakhsh. "Identification of multi-spreader users in social networks for viral marketing." Journal of Information Science 43, no. 3 (April 1, 2016): 412–23. http://dx.doi.org/10.1177/0165551516644171.

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Identifying high spreading power nodes is an interesting problem in social networks. Finding super spreader nodes becomes an arduous task when the nodes appear in large numbers, and the number of existing links becomes enormous among them. One of the methods that is used for identifying the nodes is to rank them based on k-shell decomposition. Nevertheless, one of the disadvantages of this method is that it assigns the same rank to the nodes of a shell. Another disadvantage of this method is that only one indicator is fairly used to rank the nodes. k-Shell is an approach that is used for ranking separate spreaders, yet it does not have enough efficiency when a group of nodes with maximum spreading needs to be selected; therefore, this method, alone, does not have enough efficiency. Accordingly, in this study a hybrid method is presented to identify the super spreaders based on k-shell measure. Afterwards, a suitable method is presented to select a group of superior nodes in order to maximize the spread of influence. Experimental results on seven complex networks show that our proposed methods outperforms other well-known measures and represents comparatively more accurate performance in identifying the super spreader nodes.

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Fu, Yu-Hsiang, Chung-Yuan Huang, and Chuen-Tsai Sun. "Identifying Super-Spreader Nodes in Complex Networks." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/675713.

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Identifying the most influential individuals spreading information or infectious diseases can assist or hinder information dissemination, product exposure, and contagious disease detection. Hub nodes, high betweenness nodes, high closeness nodes, and highk-shell nodes have been identified as good initial spreaders, but efforts to use node diversity within network structures to measure spreading ability are few. Here we describe a two-step framework that combines global diversity and local features to identify the most influential network nodes. Results from susceptible-infected-recovered epidemic simulations indicate that our proposed method performs well and stably in single initial spreader scenarios associated with various complex network datasets.

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Zhang, Ruixia, and Deyu Li. "Identifying Influential Rumor Spreader in Social Network." Discrete Dynamics in Nature and Society 2019 (May 15, 2019): 1–10. http://dx.doi.org/10.1155/2019/8938195.

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It is of great significance to identify influential rumor spreaders for preventing and controlling the rumor propagation. In this paper, on four real social networks, based on the classical rumor model and combining one-to-many modes of propagation, we investigate the rumor propagation by Monte Carlo simulations when the spreading rate is small. Firstly, we layer the network nodes according to network characteristics. If the assortative coefficient is positive, we layer the network nodes by the degree centrality and the nodes with large degree are in high layers. If the assortative coefficient is negative, we layer the network nodes by the K-Shell method and the nodes with large Ks value are in high layers. Then the performance of nodes in different layers as origination of rumors and as informed nodes is investigated. We find that the propagation size is larger and the peak prevalence of the rumor is reached in a shorter time when the nodes in higher layers act as origination. Moreover, when the nodes in higher layer are not the origination of the rumor, they are more likely to be informed and they are informed more faster, and they terminate propagation faster. That is, their attendance is more beneficial to propagation size, peak prevalence, and the arrival time of peak prevalence. The conclusion can provide powerful theoretical support for controlling rumor propagation or enhancing information transmission.

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Lin, Jianhong, Bo-Lun Chen, Zhao Yang, Jian-Guo Liu, and Claudio J. Tessone. "Rank the spreading influence of nodes using dynamic Markov process." New Journal of Physics 25, no. 2 (February 1, 2023): 023014. http://dx.doi.org/10.1088/1367-2630/acb590.

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Abstract Ranking the spreading influence of nodes is of great importance in practice and research. The key to ranking a node’s spreading ability is to evaluate the fraction of susceptible nodes being infected by the target node during the outbreak, i.e. the outbreak size. In this paper, we present a dynamic Markov process (DMP) method by integrating the Markov chain and the spreading process to evaluate the outbreak size of the initial spreader. Following the idea of the Markov process, this method solves the problem of nonlinear coupling by adjusting the state transition matrix and evaluating the probability of the susceptible node being infected by its infected neighbors. We have employed the susceptible-infected-recovered and susceptible-infected-susceptible models to test this method on real-world static and temporal networks. Our results indicate that the DMP method could evaluate the nodes’ outbreak sizes more accurately than previous methods for both single and multi-spreaders. Besides, it can also be employed to rank the influence of nodes accurately during the spreading process.

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Zhu, Xuejin, and Jie Huang. "SpreadRank: A Novel Approach for Identifying Influential Spreaders in Complex Networks." Entropy 25, no. 4 (April 10, 2023): 637. http://dx.doi.org/10.3390/e25040637.

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Identifying influential spreaders in complex networks is critical for information spread and malware diffusion suppression. In this paper, we propose a novel influential spreader identification method, called SpreadRank, which considers the path reachability in information spreading and uses its quantitative index as a measure of node spread centrality to obtain the spread influence of a single node. To avoid the overlapping of the influence range of the node spread, this method establishes a dynamic influential node set selection mechanism based on the spread centrality value and the principle of minimizing the maximum connected branch after network segmentation, and it selects a group of nodes with the greatest overall spread influence. Experiments based on the SIR model demonstrate that, compared to other existing methods, the selected influential spreaders of SpreadRank can quickly diffuse or suppress information more effectively.

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Saha, Sovan, Piyali Chatterjee, Mita Nasipuri, and Subhadip Basu. "Detection of spreader nodes in human-SARS-CoV protein-protein interaction network." PeerJ 9 (September 6, 2021): e12117. http://dx.doi.org/10.7717/peerj.12117.

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The entire world is witnessing the coronavirus pandemic (COVID-19), caused by a novel coronavirus (n-CoV) generally distinguished as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). SARS-CoV-2 promotes fatal chronic respiratory disease followed by multiple organ failure, ultimately putting an end to human life. International Committee on Taxonomy of Viruses (ICTV) has reached a consensus that SARS-CoV-2 is highly genetically similar (up to 89%) to the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), which had an outbreak in 2003. With this hypothesis, current work focuses on identifying the spreader nodes in the SARS-CoV-human protein–protein interaction network (PPIN) to find possible lineage with the disease propagation pattern of the current pandemic. Various PPIN characteristics like edge ratio, neighborhood density, and node weight have been explored for defining a new feature spreadability index by which spreader proteins and protein–protein interaction (in the form of network edges) are identified. Top spreader nodes with a high spreadability index have been validated by Susceptible-Infected-Susceptible (SIS) disease model, first using a synthetic PPIN followed by a SARS-CoV-human PPIN. The ranked edges highlight the path of entire disease propagation from SARS-CoV to human PPIN (up to level-2 neighborhood). The developed network attribute, spreadability index, and the generated SIS model, compared with the other network centrality-based methodologies, perform better than the existing state-of-art.

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Huo, Liang’an, Tingting Lin, Chen Liu, and Xing Fang. "How the node’s vital and tie strength effect rumor spreading on social network." International Journal of Modern Physics C 30, no. 06 (June 2019): 1950046. http://dx.doi.org/10.1142/s0129183119500463.

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The spread of rumors on complex networks has attracted wide attention in the field of management. In this paper, the generalized rumor spreading model is modified to take into account the vital of the spreader and the tie strength for the pairwise contacts between nodes in complex networks at degree-dependent spreading rate. Concretely, we introduce the infectivity exponent [Formula: see text], and the degree influenced real exponent [Formula: see text] into the analytical rumor spreading model. Rumor infectivity, [Formula: see text], where [Formula: see text], defines that each spreader node may contact [Formula: see text] neighbors within one time step. The tie strength between two nodes with degrees [Formula: see text] and [Formula: see text] are measured by [Formula: see text], [Formula: see text] is the degree influenced real exponent which depends on the type of complex networks and [Formula: see text] is a positive quantity. We use a tuning parameter [Formula: see text] to combine both the effect of the vital nodes and the strength of connectivity between nodes. We use analytical and numerical solutions to examine the threshold behavior and dynamics of the model on several models of social network. It was found that the infectivity exponent [Formula: see text], the degree influenced real exponent [Formula: see text] and tuning parameter [Formula: see text] affect the rumor threshold, one can adjust the parameters to control the rumor threshold which is absent for the standard rumor spreading model.

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Durón, Christina. "Heatmap centrality: A new measure to identify super-spreader nodes in scale-free networks." PLOS ONE 15, no. 7 (July 7, 2020): e0235690. http://dx.doi.org/10.1371/journal.pone.0235690.

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Shirly, Geofrin, and N. Kumar. "A survey on energy efficiency in mobile ad hoc networks." International Journal of Engineering & Technology 7, no. 2.21 (April 20, 2018): 382. http://dx.doi.org/10.14419/ijet.v7i2.21.12447.

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A mobile ad-hoc network is a collection of wireless mobile nodes which temporarily forms a network without any type of centralized administration which is already in use. In adhoc network, the battery lifetime of the mobile nodes is less. If suppose any nodes battery power is drained it leads to spreading of many divisions in the networks. Hence these spreader nodes are the very important spot in this network. For the purpose of data forwarding some very important nodes can drain their battery power in advance because of increase in load and processing for forwarding the data. Because of the variance in loads developed, the network of nodes will be collapsed very badly, the route lifetime will be reduced, network will be partitioned and route reliability is reduced in MANETs. Because of this, the most important criteria which have to be improved is the consumption of energy in mobile ad-hoc networks. The very important technique is power aware routing technique in MANETs. Hence minimization of energy in the network of mobile nodes individually can be done by using some of the routing techniques. The most important thing is to study the power aware protocol in order to help the new research doers and application developers to find a new idea for designing more efficient routing protocols.

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10

Mei, Xuehui, Ziyu Zhang, and Haijun Jiang. "Dynamical Analysis of Hyper-ILSR Rumor Propagation Model with Saturation Incidence Rate." Entropy 25, no. 5 (May 16, 2023): 805. http://dx.doi.org/10.3390/e25050805.

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With the development of the Internet, it is more convenient for people to obtain information, which also facilitates the spread of rumors. It is imperative to study the mechanisms of rumor transmission to control the spread of rumors. The process of rumor propagation is often affected by the interaction of multiple nodes. To reflect higher-order interactions in rumor-spreading, hypergraph theories are introduced in a Hyper-ILSR (Hyper-Ignorant–Lurker–Spreader–Recover) rumor-spreading model with saturation incidence rate in this study. Firstly, the definition of hypergraph and hyperdegree is introduced to explain the construction of the model. Secondly, the existence of the threshold and equilibrium of the Hyper-ILSR model is revealed by discussing the model, which is used to judge the final state of rumor propagation. Next, the stability of equilibrium is studied by Lyapunov functions. Moreover, optimal control is put forward to suppress rumor propagation. Finally, the differences between the Hyper-ILSR model and the general ILSR model are shown in numerical simulations.

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11

Topîrceanu, Alexandru. "Benchmarking Cost-Effective Opinion Injection Strategies in Complex Networks." Mathematics 10, no. 12 (June 15, 2022): 2067. http://dx.doi.org/10.3390/math10122067.

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Inferring the diffusion mechanisms in complex networks is of outstanding interest since it enables better prediction and control over information dissemination, rumors, innovation, and even infectious outbreaks. Designing strategies for influence maximization in real-world networks is an ongoing scientific challenge. Current approaches commonly imply an optimal selection of spreaders used to diffuse and indoctrinate neighboring peers, often overlooking realistic limitations of time, space, and budget. Thus, finding trade-offs between a minimal number of influential nodes and maximizing opinion coverage is a relevant scientific problem. Therefore, we study the relationship between specific parameters that influence the effectiveness of opinion diffusion, such as the underlying topology, the number of active spreaders, the periodicity of spreader activity, and the injection strategy. We introduce an original benchmarking methodology by integrating time and cost into an augmented linear threshold model and measure indoctrination expense as a trade-off between the cost of maintaining spreaders’ active and real-time opinion coverage. Simulations show that indoctrination expense increases polynomially with the number of spreaders and linearly with the activity periodicity. In addition, keeping spreaders continuously active instead of periodically activating them can increase expenses by 69–84% in our simulation scenarios. Lastly, we outline a set of general rules for cost-effective opinion injection strategies.

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Oostenbroek, Maurits H. W., Marco J. van der Leij, Quinten A. Meertens, Cees G. H. Diks, and Heleen M. Wortelboer. "Link-based influence maximization in networks of health promotion professionals." PLOS ONE 16, no. 8 (August 25, 2021): e0256604. http://dx.doi.org/10.1371/journal.pone.0256604.

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The influence maximization problem (IMP) as classically formulated is based on the strong assumption that “chosen” nodes always adopt the new product. In this paper we propose a new influence maximization problem, referred to as the “Link-based Influence Maximization Problem” (LIM), which differs from IMP in that the decision variable of the spreader has changed from choosing an optimal seed to selecting an optimal node to influence in order to maximize the spread. Based on our proof that LIM is NP-hard with a monotonic increasing and submodular target function, we propose a greedy algorithm, GLIM, for optimizing LIM and use numerical simulation to explore the performance in terms of spread and computation time in different network types. The results indicate that the performance of LIM varies across network types. We illustrate LIM by applying it in the context of a Dutch national health promotion program for prevention of youth obesity within a network of Dutch schools. GLIM is seen to outperform the other methods in all network types at the cost of a higher computation time. These results suggests that GLIM may be utilized to increase the effectiveness of health promotion programs.

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13

Hu, Yanqing, Shenggong Ji, Yuliang Jin, Ling Feng, H. Eugene Stanley, and Shlomo Havlin. "Local structure can identify and quantify influential global spreaders in large scale social networks." Proceedings of the National Academy of Sciences 115, no. 29 (July 3, 2018): 7468–72. http://dx.doi.org/10.1073/pnas.1710547115.

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Measuring and optimizing the influence of nodes in big-data online social networks are important for many practical applications, such as the viral marketing and the adoption of new products. As the viral spreading on a social network is a global process, it is commonly believed that measuring the influence of nodes inevitably requires the knowledge of the entire network. Using percolation theory, we show that the spreading process displays a nucleation behavior: Once a piece of information spreads from the seeds to more than a small characteristic number of nodes, it reaches a point of no return and will quickly reach the percolation cluster, regardless of the entire network structure; otherwise the spreading will be contained locally. Thus, we find that, without the knowledge of the entire network, any node’s global influence can be accurately measured using this characteristic number, which is independent of the network size. This motivates an efficient algorithm with constant time complexity on the long-standing problem of best seed spreaders selection, with performance remarkably close to the true optimum.

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Tsugawa, Sho, and Kohei Watabe. "Identifying Influential Brokers on Social Media from Social Network Structure." Proceedings of the International AAAI Conference on Web and Social Media 17 (June 2, 2023): 842–53. http://dx.doi.org/10.1609/icwsm.v17i1.22193.

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Identifying influencers in a given social network has become an important research problem for various applications, including accelerating the spread of information in viral marketing and preventing the spread of fake news and rumors. The literature contains a rich body of studies on identifying influential source spreaders who can spread their own messages to many other nodes. In contrast, the identification of influential brokers who can spread other nodes' messages to many nodes has not been fully explored. Theoretical and empirical studies suggest that involvement of both influential source spreaders and brokers is a key to facilitating large-scale information diffusion cascades. Therefore, this paper explores ways to identify influential brokers from a given social network. By using three social media datasets, we investigate the characteristics of influential brokers by comparing them with influential source spreaders and central nodes obtained from centrality measures. Our results show that (i) most of the influential source spreaders are not influential brokers (and vice versa) and (ii) the overlap between central nodes and influential brokers is small (less than 15%) in Twitter datasets. We also tackle the problem of identifying influential brokers from centrality measures and node embeddings, and we examine the effectiveness of social network features in the broker identification task. Our results show that (iii) although a single centrality measure cannot characterize influential brokers well, prediction models using node embedding features achieve F1 scores of 0.35--0.68, suggesting the effectiveness of social network features for identifying influential brokers.

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Gong, Kai, and Li Kang. "A New K-Shell Decomposition Method for Identifying Influential Spreaders of Epidemics on Community Networks." Journal of Systems Science and Information 6, no. 4 (September 26, 2018): 366–75. http://dx.doi.org/10.21078/jssi-2018-366-10.

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Abstract An efficient method for the identification of influential spreaders that could be used to control epidemics within populations would be of considerable importance. Generally, populations are characterized by its community structures and by the heterogeneous distributions of out-leaving links among nodes bridging over communities. A new method for community networks capable of identifying influential spreaders that accelerate the spread of disease is here proposed. In this method, influential spreaders serve as target nodes. This is based on the idea that, in k-shell decomposition method, out-leaving links and inner links are processed separately. The method was used on empirical networks constructed from online social networks, and results indicated that this method is more accurate. Its effectiveness stems from the patterns of connectivity among neighbors, and it successfully identified the important nodes. In addition, the performance of the method remained robust even when there were errors in the structure of the network.

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Verschakelen, J. A., P. De Leyn, J. Bogaert, and A. L. Baert. "Oncology imaging: nodal spread-intrathoracic nodes." European Radiology 6, no. 3 (June 1996): 251–61. http://dx.doi.org/10.1007/bf00180590.

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Li, Zhe, and Xinyu Huang. "Identifying Influential Spreaders Using Local Information." Mathematics 11, no. 6 (March 8, 2023): 1302. http://dx.doi.org/10.3390/math11061302.

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The heterogeneous nature indicates that different nodes may play different roles in network structure and function. Identifying influential spreaders is crucial for understanding and controlling the spread processes of epidemic, information, innovations, and so on. So how to identify influential spreaders is an urgent and crucial issue of network science. In this paper, we propose a novel local-information-based method, which can obtain the degree information of nodes’ higher-order neighbors by only considering the directly connected neighbors. Specifically, only a few iterations are needed to be executed, the degree information of nodes’ higher-order neighbors can be obtained. In particular, our method has very low computational complexity, which is very close to the degree centrality, and our method is of great extensibility, with which more factors can be taken into account through proper modification. In comparison with the well-known state-of-the-art methods, experimental analyses of the Susceptible-Infected-Recovered (SIR) propagation dynamics on ten real-world networks evidence that our method generally performs very competitively.

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Ganesan, Ghurumuruhan. "Infection Spread in Random Geometric Graphs." Advances in Applied Probability 47, no. 1 (March 2015): 164–81. http://dx.doi.org/10.1239/aap/1427814586.

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In this paper we study the speed of infection spread and the survival of the contact process in the random geometric graph G = G(n, rn, f) of n nodes independently distributed in S = [-½, ½]2 according to a certain density f(·). In the first part of the paper we assume that infection spreads from one node to another at unit rate and that infected nodes stay in the same state forever. We provide an explicit lower bound on the speed of infection spread and prove that infection spreads in G with speed at least D1nrn2. In the second part of the paper we consider the contact process ξt on G where infection spreads at rate λ > 0 from one node to another and each node independently recovers at unit rate. We prove that, for every λ > 0, with high probability, the contact process on G survives for an exponentially long time; there exist positive constants c1 and c2 such that, with probability at least 1 - c1 / n4, the contact process starting with all nodes infected survives up to time tn = exp(c2n/logn) for all n.

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Chen, Dongming, Panpan Du, Bo Fang, Dongqi Wang, and Xinyu Huang. "A Node Embedding-Based Influential Spreaders Identification Approach." Mathematics 8, no. 9 (September 10, 2020): 1554. http://dx.doi.org/10.3390/math8091554.

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Node embedding is a representation learning technique that maps network nodes into lower-dimensional vector space. Embedding nodes into vector space can benefit network analysis tasks, such as community detection, link prediction, and influential node identification, in both calculation and richer application scope. In this paper, we propose a two-step node embedding-based solution for the social influence maximization problem (IMP). The solution employs a revised network-embedding algorithm to map input nodes into vector space in the first step. In the second step, the solution clusters the vector space nodes into subgroups and chooses the subgroups’ centers to be the influential spreaders. The proposed approach is a simple but effective IMP solution because it takes both the social reinforcement and homophily characteristics of the social network into consideration in node embedding and seed spreaders selection operation separately. The information propagation simulation experiment of single-point contact susceptible-infected-recovered (SIR) and full-contact SIR models on six different types of real network data sets proved that the proposed social influence maximization (SIM) solution exhibits significant propagation capability.

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Ganesan, Ghurumuruhan. "Infection Spread in Random Geometric Graphs." Advances in Applied Probability 47, no. 01 (March 2015): 164–81. http://dx.doi.org/10.1017/s0001867800007758.

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In this paper we study the speed of infection spread and the survival of the contact process in the random geometric graph G = G(n, r n , f) of n nodes independently distributed in S = [-½, ½]2 according to a certain density f(·). In the first part of the paper we assume that infection spreads from one node to another at unit rate and that infected nodes stay in the same state forever. We provide an explicit lower bound on the speed of infection spread and prove that infection spreads in G with speed at least D 1 nr n 2. In the second part of the paper we consider the contact process ξ t on G where infection spreads at rate λ > 0 from one node to another and each node independently recovers at unit rate. We prove that, for every λ > 0, with high probability, the contact process on G survives for an exponentially long time; there exist positive constants c 1 and c 2 such that, with probability at least 1 - c 1 / n 4, the contact process starting with all nodes infected survives up to time t n = exp(c 2 n/logn) for all n.

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Lee, Jae Hoon, Kyeong Geun Lee, Tae Kyung Ha, Young Jin Jun, Seung Sam Pair, Hwon Kyum Park, and Kwang Soo Lee. "Pattern Analysis of Lymph Node Metastasis and the Prognostic Importance of Number of Metastatic Nodes in Ampullary Adenocarcinoma." American Surgeon 77, no. 3 (March 2011): 322–29. http://dx.doi.org/10.1177/000313481107700322.

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The aims of this study were to clarify the distribution and spread pattern of metastatic nodes and to evaluate the importance of the number, ratio, and location of positive nodes in ampullary adenocarcinoma. We analyzed the clinicopathologic data and survival of 52 patients who received curative pancreatoduodenectomy for ampullary adenocarcinoma between June 1994 and May 2009. Metastatic lymph nodes were found in 32 (61.5%) patients. The median number of evaluated nodes and positive nodes were 26 (range 10-60) and two (range 1-15), respectively. The most commonly involved nodes were the posterior pancreaticoduodenal nodes (26 patients) followed by the anterior pancreaticoduodenal nodes (11 patients). No positive hepatoduodenal and common hepatic artery nodes were found. In univariate analysis, number of positive nodes, and their ratio and location were significantly associated with survival. Only the factor of three or more metastatic nodes had the independent power in predicting a poor outcome in multivariate analysis ( P < 0.001). Ampullary adenocarcinoma first spreads to the posterior pancreaticoduodenal nodes and then the anterior nodes. The number of positive lymph nodes, rather than their ratio and location, independently affects survival after curative resection in patients with ampullary carcinoma.

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Huang, Haojing, Zhiming Cui, and Shukui Zhang. "A Spread Willingness Computing-Based Information Dissemination Model." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/680421.

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This paper constructs a kind of spread willingness computing based on information dissemination model for social network. The model takes into account the impact of node degree and dissemination mechanism, combined with the complex network theory and dynamics of infectious diseases, and further establishes the dynamical evolution equations. Equations characterize the evolutionary relationship between different types of nodes with time. The spread willingness computing contains three factors which have impact on user’s spread behavior: strength of the relationship between the nodes, views identity, and frequency of contact. Simulation results show that different degrees of nodes show the same trend in the network, and even if the degree of node is very small, there is likelihood of a large area of information dissemination. The weaker the relationship between nodes, the higher probability of views selection and the higher the frequency of contact with information so that information spreads rapidly and leads to a wide range of dissemination. As the dissemination probability and immune probability change, the speed of information dissemination is also changing accordingly. The studies meet social networking features and can help to master the behavior of users and understand and analyze characteristics of information dissemination in social network.

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Hu, Zhao-Long, Zhuo-Ming Ren, Guang-Yong Yang, and Jian-Guo Liu. "Effects of multiple spreaders in community networks." International Journal of Modern Physics C 25, no. 05 (March 11, 2014): 1440013. http://dx.doi.org/10.1142/s0129183114400130.

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Human contact networks exhibit the community structure. Understanding how such community structure affects the epidemic spreading could provide insights for preventing the spreading of epidemics between communities. In this paper, we explore the spreading of multiple spreaders in community networks. A network based on the clustering preferential mechanism is evolved, whose communities are detected by the Girvan–Newman (GN) algorithm. We investigate the spreading effectiveness by selecting the nodes as spreaders in the following ways: nodes with the largest degree in each community (community hubs), the same number of nodes with the largest degree from the global network (global large-degree) and randomly selected one node within each community (community random). The experimental results on the SIR model show that the spreading effectiveness based on the global large-degree and community hubs methods is the same in the early stage of the infection and the method of community random is the worst. However, when the infection rate exceeds the critical value, the global large-degree method embodies the worst spreading effectiveness. Furthermore, the discrepancy of effectiveness for the three methods will decrease as the infection rate increases. Therefore, we should immunize the hubs in each community rather than those hubs in the global network to prevent the outbreak of epidemics.

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Sheikhahmadi, Adib, Farshid Veisi, Amir Sheikhahmadi, and Shahnaz Mohammadimajd. "A multi-attribute method for ranking influential nodes in complex networks." PLOS ONE 17, no. 11 (November 28, 2022): e0278129. http://dx.doi.org/10.1371/journal.pone.0278129.

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Calculating the importance of influential nodes and ranking them based on their diffusion power is one of the open issues and critical research fields in complex networks. It is essential to identify an attribute that can compute and rank the diffusion power of nodes with high accuracy, despite the plurality of nodes and many relationships between them. Most methods presented only use one structural attribute to capture the influence of individuals, which is not entirely accurate in most networks. The reason is that network structures are disparate, and these methods will be inefficient by altering the network. A possible solution is to use more than one attribute to examine the characteristics aspect and address the issue mentioned. Therefore, this study presents a method for identifying and ranking node’s ability to spread information. The purpose of this study is to present a multi-attribute decision making approach for determining diffusion power and classification of nodes, which uses several local and semi-local attributes. Local and semi-local attributes with linear time complexity are used, considering different aspects of the network nodes. Evaluations performed on datasets of real networks demonstrate that the proposed method performs satisfactorily in allocating distinct ranks to nodes; moreover, as the infection rate of nodes increases, the accuracy of the proposed method increases.

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Yang, Li, Yu-Rong Song, Guo-Ping Jiang, and Ling-Ling Xia. "Identifying influential spreaders based on diffusion K-truss decomposition." International Journal of Modern Physics B 32, no. 22 (August 20, 2018): 1850238. http://dx.doi.org/10.1142/s0217979218502387.

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Identifying the most influential spreaders is important in optimizing the network structure or disseminating information through networks. Recent study showed that the K-truss decomposition could filter out the nodes that performed a worse spreading behavior in the maximal K-shell subgraph. The spreaders belonging to the maximal K-truss subgraph show better performance compared to previously used importance criteria. However, the accuracy of the K-truss or the K-shell in determining node coreness is largely susceptible to core-like group. In this paper, we propose an improved diffusion K-truss decomposition method by considering both the diffusion and clustering of edges to eliminate the impact of core-like group on identifying influential nodes. To validate the effectiveness of the proposed method, we compare it with five typical methods by carrying out Monte–Carlo simulations over six real complex networks. Simulation results demonstrate that the proposed method can effectively disintegrate the core-like group and accurately identify the influential nodes.

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Zhang, Xiaorong, Sanyang Liu, and Yudong Gong. "A New Strategy in Boosting Information Spread." Entropy 24, no. 4 (April 2, 2022): 502. http://dx.doi.org/10.3390/e24040502.

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Finding a seed set to propagate more information within a specific budget is defined as the influence maximization (IM) problem. The traditional IM model contains two cardinal aspects: (i) the influence propagation model and (ii) effective/efficient seed-seeking algorithms. However, most of models only consider one kind of node (i.e., influential nodes), ignoring the role of other nodes (e.g., boosting nodes) in the spreading process, which are irrational. Specifically, in the real-world propagation scenario, the boosting nodes always improve the spread of influence from the initial activated seeds, which is an efficient and cost-economic measure. In this paper, we consider the realistic budgeted influence maximization (RBIM) problem, which contains two kind of nodes to improve the diffusion of influence. Facing the newly modified objective function, we propose a novel B-degree discount algorithm to solve it. The novel B-degree discount algorithm which adopts the cost-economic boosting nodes to retweet the influence from the predecessor nodes can greatly reduce the cost, and performs better than other state-of-the-art algorithms in both effect and efficiency on RBIM problem solving.

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Escobar, Pedro F. "Lymphatic Spread of Endometriosis to Para-Aortic Nodes." Journal of Minimally Invasive Gynecology 20, no. 6 (November 2013): 741. http://dx.doi.org/10.1016/j.jmig.2013.02.014.

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Liu, Yong, Zijun Cheng, Xiaoqin Li, and Zongshui Wang. "An Entropy-Based Gravity Model for Influential Spreaders Identification in Complex Networks." Complexity 2023 (April 30, 2023): 1–19. http://dx.doi.org/10.1155/2023/6985650.

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The mining of key nodes is an important topic in complex network research, which can help identify influencers. The study is necessary for blocking the spread of epidemics, controlling public opinion, and managing transportation. The techniques thus far suggested have a lot of drawbacks; they either depend on the regional distribution of nodes or the global character of the network. The gravity formula based on node information is a good mathematical model that can represent the magnitude of attraction between nodes. However, the gravity model requires less node information and has limitations. In this study, we propose a gravity model based on Shannon entropy to effectively address the aforementioned issues. The spreading probability method is employed to enhance the model’s functionality and applicability. Through testing, it has been determined that the suggested model is a good alternative to the gravity model for selecting influential nodes.

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Kurt, İdris. "A mass mimicking primary pancreatic malignancy." Kastamonu Medical Journal 3, no. 2 (June 26, 2023): 113–15. http://dx.doi.org/10.51271/kmj-0110.

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Colorectal cancer ranks third among all cancers in terms of prevalence. It is the second most common cause of death overall. The patterns of spread are lymphatic, hematogenous, and direct invasion. Lymph node involvement is a prognostic factor and has a strong correlation with survival and disease free interval. Regional lymph nodes are frequently the first structures to get affected. Metastatic disease (stage IV) is defined as the spread of disease to lymph nodes other than regional ones. Because of this, the type of treatment that is administered and the patient's survival rate are both affected when the cancer spreads to non-regional lymph nodes. In this particular instance, we report a patient who had results that were consistent with having a pancreatic tumor. Nevertheless, with the aid of endoscopic ultrasonography, we determined that the patient had metastatic colon cancer. And the patient's trajectory takes a dramatic turn for the worse, shifting from resectable pancreatic cancer to metastatic colon cancer.

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Ma, Xiaojian, and Yinghong Ma. "The Local Triangle Structure Centrality Method to Rank Nodes in Networks." Complexity 2019 (January 2, 2019): 1–16. http://dx.doi.org/10.1155/2019/9057194.

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Detecting influential spreaders had become a challenging and crucial topic so far due to its practical application in many areas, such as information propagation inhibition and disease dissemination control. Some traditional local based evaluation methods had given many discussions on ranking important nodes. In this paper, ranking nodes of networks continues to be discussed. A semilocal structures method for ranking nodes based on the degree and the neighbors’ connections of the node is presented. The semilocal structures are regarded as the number of neighbors of the nodes and the connections between the node and its neighbors. We combined the triangle structure and the degree information of the neighbors to define the inner-outer spreading ability of the nodes and then summed the node neighbors’ inner-outer spreading ability to be used as the local triangle structure centrality (LTSC). The LTSC avoids the defect “pseudo denser connections” in measuring the structure of neighbors. The performance of the proposed LTSC method is evaluated by comparing the spreading ability on both real-world and synthetic networks with the SIR model. The simulation results of the discriminability and the correctness compared with pairs of ranks (one is generated by SIR model and the others are generated by central nodes measures) show that LTSC outperforms some other local or semilocal methods in evaluating the node’s influence in most cases, such as degree, betweenness, H-index, local centrality, local structure centrality, K-shell, and S-shell. The experiments prove that the LTSC is an efficient and accurate ranking method which provides a more reasonable evaluating index to rank nodes than some previous approaches.

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Kukkala, Varsha Bhat, and S. R. S. Iyengar. "Identifying Influential Spreaders in a Social Network (While Preserving Privacy)." Proceedings on Privacy Enhancing Technologies 2020, no. 2 (April 1, 2020): 537–57. http://dx.doi.org/10.2478/popets-2020-0040.

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AbstractIn order to disseminate information in a social network, it is important to first identify the influential spreaders in the network. Using them as the seed spreaders, the aim is to ensure that the information is cascaded throughout the network. The traditional approach to identifying influential nodes is to determine the top-r ranked nodes in accordance with various ranking methods such as PageRank, k-Shell decomposition, ClusterRank and VoteRank. In the current work, we study the problem of ranking the nodes when the underlying graph is distributedly held by a set of individuals, who consider their share of the data as private information. In particular, we design efficient secure multiparty computation (MPC) protocols for k-Shell decomposition, PageRank and VoteRank. For improved efficiency, we employ the oblivious RAM construct in conjunction with efficient data-oblivious graph data structures. We are the first to propose a secure variant of the VoteRank algorithm. We prove that the proposed protocols are asymptotically more efficient and have lower runtime in practice than the previous best known MPC protocols for computing k-Shell decomposition and PageRank centrality scores.

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Rajeh, Stephany, and Hocine Cherifi. "Ranking influential nodes in complex networks with community structure." PLOS ONE 17, no. 8 (August 29, 2022): e0273610. http://dx.doi.org/10.1371/journal.pone.0273610.

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Quantifying a node’s importance is decisive for developing efficient strategies to curb or accelerate any spreading phenomena. Centrality measures are well-known methods used to quantify the influence of nodes by extracting information from the network’s structure. The pitfall of these measures is to pinpoint nodes located in the vicinity of each other, saturating their shared zone of influence. In this paper, we propose a ranking strategy exploiting the ubiquity of the community structure in real-world networks. The proposed community-aware ranking strategy naturally selects a set of distant spreaders with the most significant influence in the networks. One can use it with any centrality measure. We investigate its effectiveness using real-world and synthetic networks with controlled parameters in a Susceptible-Infected-Recovered (SIR) diffusion model scenario. Experimental results indicate the superiority of the proposed ranking strategy over all its counterparts agnostic about the community structure. Additionally, results show that it performs better in networks with a strong community structure and a high number of communities of heterogeneous sizes.

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Watanabe, Yoh, Junzo Shimizu, Makoto Tsubota, and Takashi Iwa. "Mediastinal Spread of Metastatic Lymph Nodes in Bronchogenic Carcinoma." Chest 97, no. 5 (May 1990): 1059–65. http://dx.doi.org/10.1378/chest.97.5.1059.

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LI, CHONG, SHI-ZE GUO, ZHE-MING LU, YU-LONG QIAO, and GUANG-HUA SONG. "A NEW CENTRALITY METRIC BASED ON CLUSTERING COEFFICIENT." International Journal of Modern Physics C 24, no. 07 (June 6, 2013): 1350043. http://dx.doi.org/10.1142/s0129183113500435.

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Many centrality metrics have been proposed over the years to compute the centrality of nodes, which has been a key issue in complex network analysis. The most important node can be estimated through a variety of metrics, such as degree, closeness, eigenvector, betweenness, flow betweenness, cumulated nominations and subgraph. Simulated flow is a common method adopted by many centrality metrics, such as flow betweenness centrality, which assumes that the information spreads freely in the entire network. Generally speaking, the farther the information travels, the more times the information passes the geometric center. Thus, it is easy to determine which node is more likely to be the center of the geometry network. However, during information transmission, different nodes do not share the same vitality, and some nodes are more active than others. Therefore, the product of one node's degree and its clustering coefficient can be viewed as a good factor to show how active this node is. In this paper, a new centrality metric called vitality centrality is introduced, which is only based on this product and the simulated flow. Simulation experiments based on six test networks have been carried out to demonstrate the effectiveness of our new metric.

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Liu, Dong, Yun Jing, and Baofang Chang. "Identifying influential nodes in complex networks based on expansion factor." International Journal of Modern Physics C 27, no. 09 (August 2016): 1650105. http://dx.doi.org/10.1142/s0129183116501059.

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Identifying the top influential spreaders in a network has practical significance. In this paper, we propose a novel centrality to identify influential spreaders based on expansion factor. Nodes with high expansion factor centrality (EFC) have strong spreading capability. During the course of the work, an improved strategy is proposed to reduce the time complexity of EFC. We discuss the correlations between EFC and the other five classical indicators. Simulation results on the Susceptible-Infected-Removed (SIR) model manifest that EFC can identify influential nodes and find some critical influential nodes neglected by other indicators.

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Yanez-Sierra, Jedidiah, Arturo Diaz-Perez, and Victor Sosa-Sosa. "An Efficient Partition-Based Approach to Identify and Scatter Multiple Relevant Spreaders in Complex Networks." Entropy 23, no. 9 (September 15, 2021): 1216. http://dx.doi.org/10.3390/e23091216.

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One of the main problems in graph analysis is the correct identification of relevant nodes for spreading processes. Spreaders are crucial for accelerating/hindering information diffusion, increasing product exposure, controlling diseases, rumors, and more. Correct identification of spreaders in graph analysis is a relevant task to optimally use the network structure and ensure a more efficient flow of information. Additionally, network topology has proven to play a relevant role in the spreading processes. In this sense, more of the existing methods based on local, global, or hybrid centrality measures only select relevant nodes based on their ranking values, but they do not intentionally focus on their distribution on the graph. In this paper, we propose a simple yet effective method that takes advantage of the underlying graph topology to guarantee that the selected nodes are not only relevant but also well-scattered. Our proposal also suggests how to define the number of spreaders to select. The approach is composed of two phases: first, graph partitioning; and second, identification and distribution of relevant nodes. We have tested our approach by applying the SIR spreading model over nine real complex networks. The experimental results showed more influential and scattered values for the set of relevant nodes identified by our approach than several reference algorithms, including degree, closeness, Betweenness, VoteRank, HybridRank, and IKS. The results further showed an improvement in the propagation influence value when combining our distribution strategy with classical metrics, such as degree, outperforming computationally more complex strategies. Moreover, our proposal shows a good computational complexity and can be applied to large-scale networks.

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Aranovich, David, Karen Meir, Michal M. Lotem, Liat Appelbaum, and Hadar Merhav. "Ocular Melanoma Metastasizing to Intra-Abdominal Lymph Nodes." Case Reports in Surgery 2013 (2013): 1–4. http://dx.doi.org/10.1155/2013/534730.

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Background. Visceral metastatic spread of ocular melanoma most commonly occurs via hematogenous route to the liver. Lymphatic spread of ocular melanoma into abdominal lymph nodes has not been reported previously.Case Presentation. A 47-year-old man with a history of ocular melanoma presented with a soft tissue mass on CT scan. The mass encased the portal structures of the hepaticoduodenal ligament. Image-guided biopsy revealed it to be a metastatic melanoma to lymph nodes. The patient underwent surgery with the intent to prolong disease-free survival. On final pathological examination, two lymph nodes were found harboring metastatic melanoma.Conclusion. Extrahepatic lymphatic intra-abdominal spread of ocular melanoma is not impossible. Since this mode of spread is rare, the oncologic significance of surgical resection of isolated intra-abdominal nodal with metastatic ocular melanoma is difficult to determine at the present time.

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Wang Xiao-Yang, Wang Ying, Zhu Can-Shi, Zhu Lin, and Fu Chao-Qi. "Information radiation model with across neighbor spread abilities of nodes." Acta Physica Sinica 66, no. 3 (2017): 038901. http://dx.doi.org/10.7498/aps.66.038901.

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Hirabayashi, Hideki, Eenji Koshii, Kohei Uno, Haruyuki Ohgaki, Yasuji Nakasone, Tutomu Fujisawa, Noboru Syouno, Tadashi Hinohara, and Kaoru Hirabayashi. "Extracapsular Spread of Squamous Cell Carcinoma in Neck Lymph Nodes." Laryngoscope 101, no. 5 (May 1991): 502???506. http://dx.doi.org/10.1288/00005537-199105000-00010.

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Király, Gábor, Zoltán Hargitai, Ilona Kovács, Gábor Szemán-Nagy, István Juhász, and Gáspár Bánfalvi. "Metastatic Spread from Abdominal Tumor Cells to Parathymic Lymph Nodes." Pathology & Oncology Research 25, no. 2 (November 7, 2018): 625–33. http://dx.doi.org/10.1007/s12253-018-0492-7.

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Nomura, Tsutomu, Masahiko Onda, Masao Miyashita, Hiroshi Makino, Hiroshi Maruyama, Shigenao Nagasawa, Ryouhei Futami, Kiyohiko Yamashita, Kaiyo Takubo, and Koji Sasajima. "Wide-spread Distribution of Sentinel Lymph nodes in Esophageal Cancer." Journal of Nippon Medical School 68, no. 5 (2001): 393–96. http://dx.doi.org/10.1272/jnms.68.393.

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42

Sathish, Kaveripakam, Monia Hamdi, Ravikumar Chinthaginjala, Giovanni Pau, Amel Ksibi, Rajesh Anbazhagan, Mohamed Abbas, and Mohammed Usman. "Reliable Data Transmission in Underwater Wireless Sensor Networks Using a Cluster-Based Routing Protocol Endorsed by Member Nodes." Electronics 12, no. 6 (March 8, 2023): 1287. http://dx.doi.org/10.3390/electronics12061287.

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Considering Underwater Wireless Sensor Networks (UWSNs) have limited power resources (low bandwidth, long propagation delays, and non-rechargeable batteries), it is critical that they develop solutions to reduce power usage. Clustering is one solution because it not only saves energy consumption but also improves scalability and data integrity. The design of UWSNs is vital to the development of clustering algorithms. The limited energy of sensor nodes, narrow transmission bandwidth, and unpredictable topology of mobile Underwater Acoustic Wireless Sensor Networks (UAWSNs) make it challenging to build an effective and dependable underwater communication network. Despite its success in data dependability, the acoustic underwater communication channel consumes the greatest energy at a node. Recharging and replacing a submerged node’s battery could be prohibitively expensive. We propose a network architecture called Member Nodes Supported Cluster-Based Routing Protocol (MNS-CBRP) to achieve consistent information transfer speeds by using the network’s member nodes. As a result, we use clusters, which are produced by dividing the network’s space into many minute circular sections. Following that, a Cluster Head (CH) node is chosen for each circle. Despite the fact that the source nodes are randomly spread, all of the cluster heads are linked to the circle’s focal point. It is the responsibility of the MNS-CBRP source nodes to communicate the discovered information to the CH. The discovered data will then be sent to the CH that follows it, and so on, until all data packets have been transferred to the surface sinks. We tested our techniques thoroughly using QualNet Simulator to determine their viability.

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Xin, Yingchu, Chao Gao, Zhen Wang, Xiyuan Zhen, and Xianghua Li. "Discerning Influential Spreaders in Complex Networks by Accounting the Spreading Heterogeneity of the Nodes." IEEE Access 7 (2019): 92070–78. http://dx.doi.org/10.1109/access.2019.2927775.

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Ngo, Shin-Chieng, Allon G. Percus, Keith Burghardt, and Kristina Lerman. "The transsortative structure of networks." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 476, no. 2237 (May 2020): 20190772. http://dx.doi.org/10.1098/rspa.2019.0772.

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Network topologies can be highly non-trivial, due to the complex underlying behaviours that form them. While past research has shown that some processes on networks may be characterized by local statistics describing nodes and their neighbours, such as degree assortativity, these quantities fail to capture important sources of variation in network structure. We define a property called transsortativity that describes correlations among a node’s neighbours. Transsortativity can be systematically varied, independently of the network’s degree distribution and assortativity. Moreover, it can significantly impact the spread of contagions as well as the perceptions of neighbours, known as the majority illusion. Our work improves our ability to create and analyse more realistic models of complex networks.

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Topf, Michael C., Larry Harshyne, Madalina Tuluc, Stacey Mardekian, Swar Vimawala, David M. Cognetti, Joseph M. Curry, Ulrich Rodeck, and Adam Luginbuhl. "Loss of CD169+ Subcapsular Macrophages during Metastatic Spread of Head and Neck Squamous Cell Carcinoma." Otolaryngology–Head and Neck Surgery 161, no. 1 (February 12, 2019): 67–73. http://dx.doi.org/10.1177/0194599819829741.

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Objective The purpose of this study is to assess CD169 expression in metastatic and nearby tumor-free lymph nodes of patients with head and neck squamous cell carcinoma (SCC). Study Design Retrospective analysis based on immunohistochemistry. Setting Tertiary care center. Subjects and Methods The abundance of CD169+ cells in the subcapsular sinuses (SCSs) of lymph nodes was assessed immunohistochemically in paraffin-embedded tissue samples derived from 22 patients with oral cavity and oropharyngeal SCC. Results SCSs of lymph nodes harboring metastatic SCC contained significantly fewer CD169+ macrophages (106.5 ± 113.6 cells/mm2) compared to nearby tumor-free lymph nodes (321.3 ± 173.4 cells/mm2, P < .001). This observation extended to 21 of the 22 cases investigated. In addition, 6 patients who later developed recurrent disease contained lower numbers of CD169+ cells (268.6 ± 169.5 cells/mm2) in nearby tumor-free lymph nodes compared to 341.0 ± 176.1 cells/mm2 in those who remained disease free ( P = .399). Human papillomavirus (HPV)–positive patients (n = 4) had a 6-fold lower number of CD169+ cells in metastatic nodes (61.2 ± 85.5 cells/mm2) compared to nearby tumor-free lymph nodes (369.5 ± 175.5 cells/mm2, P = .028). In comparison, HPV-negative patients had only a 3-fold reduction (116.6 ± 118.5 cells/mm2 vs 310.6 ± 176.2 cells/mm2, P < .001). Conclusion Metastatic spread of SCC to regional lymph nodes is associated with lower abundance of CD169+ macrophages in the SCSs of draining lymph nodes. These results set the stage for an in-depth investigation into the mechanism(s) by which metastatic SCC controls CD169+ macrophage abundance and its significance as it relates to prognosis and treatment response.

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Simon, Marek, Ladislav Huraj, Iveta Dirgova Luptakova, and Jiri Pospichal. "How to Burn a Network or Spread Alarm." MENDEL 25, no. 2 (December 20, 2019): 11–18. http://dx.doi.org/10.13164/mendel.2019.2.011.

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This paper compares centrality indices usage within a heuristic method for a fast spread of alarm, or crucial information. Such indices can be used as a core part within more sophisticated optimisation methods, which should determine a graph parameter - burning number, defining, how fast can an alarm spread through all nodes. In this procedure at each time step a new chosen node is alarmed (i.e. burned) “from outside”, and already alarmed nodes at each time step then alarm their neighbours. The procedure ends, when all the nodes are alarmed (i.e. burned). The optimisation heuristic should choose such ordered sequence of nodes, which are to be alarmed “from outside”, that their number, equal the number of time steps (i.e. burning number) necessary to alarm the whole network, is minimised. The NP completeness of the problem necessitates a usage of heuristics. However, even the heuristics can be slower, reaching towards a global optimum, or faster, exchanging part of the quality for a time. This paper studies the usage of centrality indices in a simpler and faster heuristic. It should be useful e.g. for a mobile network of cars or drones, when an optimal solution cannot be computed in advance, or take too much CPU time, since the connections within the dynamic network might not exist any longer. A wide range of centrality indices was tested on selected networks, both real as well as artificially generated. While the performances of indices substantially differ for different types of networks, results show, which centrality indices work well across all tested networks.

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Wu, Hong, Zhijian Zhang, Yabo Fang, Shaotang Zhang, Zuo Jiang, Jian Huang, and Ping Li. "Containment of rumor spread by selecting immune nodes in social networks." Mathematical Biosciences and Engineering 18, no. 3 (2021): 2614–31. http://dx.doi.org/10.3934/mbe.2021133.

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Yemshanov, Denys, Ning Liu, Daniel K. Thompson, Marc-André Parisien, Quinn E. Barber, Frank H. Koch, and Jonathan Reimer. "Detecting critical nodes in forest landscape networks to reduce wildfire spread." PLOS ONE 16, no. 10 (October 7, 2021): e0258060. http://dx.doi.org/10.1371/journal.pone.0258060.

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Although wildfires are an important ecological process in forested regions worldwide, they can cause significant economic damage and frequently create widespread health impacts. We propose a network optimization approach to plan wildfire fuel treatments that minimize the risk of fire spread in forested landscapes under an upper bound for total treated area. We used simulation modeling to estimate the probability of fire spread between pairs of forest sites and formulated a modified Critical Node Detection (CND) model that uses these estimated probabilities to find a pattern of fuel reduction treatments that minimizes the likely spread of fires across a landscape. We also present a problem formulation that includes control of the size and spatial contiguity of fuel treatments. We demonstrate the approach with a case study in Kootenay National Park, British Columbia, Canada, where we investigated prescribed burn options for reducing the risk of wildfire spread in the park area. Our results provide new insights into cost-effective planning to mitigate wildfire risk in forest landscapes. The approach should be applicable to other ecosystems with frequent wildfires.

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Ulmer, Anja, Klaus Dietz, Isabelle Hodak, Bernhard Polzer, Sebastian Scheitler, Murat Yildiz, Zbigniew Czyz, et al. "Quantitative Measurement of Melanoma Spread in Sentinel Lymph Nodes and Survival." PLoS Medicine 11, no. 2 (February 18, 2014): e1001604. http://dx.doi.org/10.1371/journal.pmed.1001604.

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DITHMAR, STEFAN, CARLOS E. DIAZ, and HANS E. GROSSNIKLAUS. "INTRAOCULAR MELANOMA SPREAD TO REGIONAL LYMPH NODES: Report of Two Cases." Retina 20, no. 1 (January 2000): 76–79. http://dx.doi.org/10.1097/00006982-200001000-00014.

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Journal articles on the topic 'SPREADER NODES'

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