Measuring centrality in a social network, especially in bipartite mode, poses many challenges, for example, the requirement of full knowledge of the network topology, and the lack of properly detecting top-k behavioral representative users. To overcome the above mentioned challenges, we propose HellRank, an accurate centrality measure for identifying central nodes in bipartite social networks. HellRank is based on the Hellinger distance between two nodes on the same side of a bipartite network. We theoretically analyze the impact of this distance on a bipartite network and find upper and lower bounds for it. The computation of the HellRank centrality measure can be distributed, by letting each node uses local information only on its immediate neighbors. Consequently, one does not need a central entity that has full knowledge of the network topological structure. We experimentally evaluate the performance of the HellRank measure in correlation with other centrality measures on real-world networks. The results show partial ranking similarity between the HellRank and the other conventional metrics according to the Kendall and Spearman rank correlation coefficient.
@cite_57 studied how to infer social probabilities of influence by developing an algorithm to scan over the log of actions of social network users using real data. @cite_14 @cite_17 focused on the influence maximization problem to model the social influence on large networks. TwitterRank, as an extension of PageRank metric, was proposed by @cite_60 to identify influential users in Twitter. @cite_83 used the Susceptible-Infected-Recovered (SIR) model to examine the spreading influence of the nodes ranked by different influence measures. @cite_23 identified influencers using joint influence powers through Influence network. @cite_15 identified influencial users by using user trust networks. @cite_32 proposed the weighted LeaderRank technique by replacing the standard random walk to a biased random walk. @cite_29 presented a novel analysis on the statistical simplex as a manifold with boundary and applied the proposed technique to social network analysis to rank a subset of influencer nodes. @cite_40 proposed a new approach to incorporate users' reply relationship, conversation content and response immediacy to identify influential users of online health care community. @cite_49 used multi-attribute and homophily characteristics in a new method to identify influential nodes in complex networks.
@cite_14 @cite_60 @cite_29 @cite_32 @cite_57 @cite_40 @cite_83 @cite_23 @cite_49 @cite_15 @cite_17
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Social networks often serve as a medium for the diffusion of ideas or innovations. An individual's decision whether to adopt a product or innovation will be highly dependent on the choices made by the individual's peers or neighbors in the social network. In this work, we study the game of innovation diffusion with multiple competing innovations such as when multiple companies market competing products using viral marketing. Our first contribution is a natural and mathematically tractable model for the diffusion of multiple innovations in a network. We give a (1-1 e) approximation algorithm for computing the best response to an opponent's strategy, and prove that the "price of competition" of this game is at most 2. We also discuss "first mover" strategies which try to maximize the expected diffusion against perfect competition. Finally, we give an FPTAS for the problem of maximizing the influence of a single player when the underlying graph is a tree. This paper focuses on the problem of identifying influential users of micro-blogging services. Twitter, one of the most notable micro-blogging services, employs a social-networking model called "following", in which each user can choose who she wants to "follow" to receive tweets from without requiring the latter to give permission first. In a dataset prepared for this study, it is observed that (1) 72.4 of the users in Twitter follow more than 80 of their followers, and (2) 80.5 of the users have 80 of users they are following follow them back. Our study reveals that the presence of "reciprocity" can be explained by phenomenon of homophily. Based on this finding, TwitterRank, an extension of PageRank algorithm, is proposed to measure the influence of users in Twitter. TwitterRank measures the influence taking both the topical similarity between users and the link structure into account. Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank. Sparsity in R^m has been widely explored in machine learning. We study sparsity on a statistical simplex consisting of all categorical distributions. This is different from the case in R^m because such a simplex is a Riemannian manifold, a curved space. A learner with sparse constraints should be likely to fall to its low-dimensional boundaries. We present a novel analysis on the statistical simplex as a manifold with boundary. The main contribution is an explicit view of the learning dynamics in between high-dimensional models in the interior of the simplex and low-dimensional models on its boundaries. We prove the differentiability of the cost function, the natural gradient with respect to the Riemannian structure, and convexity around the singular regions. We uncover an interesting relationship with L1 regularization. We apply the proposed technique to social network analysis. Given a directed graph, the task is to rank a subset of influencer nodes. Here, sparsity means that the top-ranked nodes should present diversity in the sense of minimizing influence overlap. We present a ranking algorithm based on the natural gradient. It can scale up to graph datasets with millions of nodes. On real large networks, the top-ranked nodes are the most informative among several commonly-used techniques. Identifying influential spreaders is crucial for understanding and controlling spreading processes on social networks. Via assigning degree-dependent weights onto links associated with the ground node, we proposed a variant to a recent ranking algorithm named LeaderRank (, 2011). According to the simulations on the standard SIR model, the weighted LeaderRank performs better than LeaderRank in three aspects: (i) the ability to find out more influential spreaders; (ii) the higher tolerance to noisy data; and (iii) the higher robustness to intentional attacks. Recently, there has been tremendous interest in the phenomenon of influence propagation in social networks. The studies in this area assume they have as input to their problems a social graph with edges labeled with probabilities of influence between users. However, the question of where these probabilities come from or how they can be computed from real social network data has been largely ignored until now. Thus it is interesting to ask whether from a social graph and a log of actions by its users, one can build models of influence. This is the main problem attacked in this paper. In addition to proposing models and algorithms for learning the model parameters and for testing the learned models to make predictions, we also develop techniques for predicting the time by which a user may be expected to perform an action. We validate our ideas and techniques using the Flickr data set consisting of a social graph with 1.3M nodes, 40M edges, and an action log consisting of 35M tuples referring to 300K distinct actions. Beyond showing that there is genuine influence happening in a real social network, we show that our techniques have excellent prediction performance. Due to the revolutionary development of Web 2.0 technology, individual users have become major contributors of Web content in online social media. In light of the growing activities, how to measure a user’s influence to other users in online social media becomes increasingly important. This research need is urgent especially in the online healthcare community since positive influence can be beneficial while negative influence may cause-negative impact on other users of the same community. In this article, a research framework was proposed to study user influence within the online healthcare community. We proposed a new approach to incorporate users’ reply relationship, conversation content and response immediacy which capture both explicit and implicit interaction between users to identify influential users of online healthcare community. A weighted social network is developed to represent the influence between users. We tested our proposed techniques thoroughly on two medical support forums. Two algorithms UserRank and Weighted in-degree are benchmarked with PageRank and in-degree. Experiment results demonstrated the validity and effectiveness of our proposed approaches. Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node’s neighbors but do not directly make use of the interactions among it’s neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors’ influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR) spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about nodes, more than 15 times faster than PageRank. With the tremendous popularity of social networking sites in this era of Web 2.0, increasingly more users are contributing their comments and opinions about products, people, organizations, and many other entities. These online comments often have direct influence on consumers' buying decisions and the public's impressions of enterprises. As a result, enterprises have begun to explore the feasibility of using social networking sites as platforms to conduct targeted marking and enterprise reputation management for e-commerce and e-business. As indicated from recent marketing research, the joint influential power of a small group of active users could have considerable impact on a large number of consumers' buying decisions and the public's perception of the capabilities of enterprises. This paper illustrates a novel method that can effectively discover the most influential users from social networking sites (SNS). In particular, the general method of mining the influence network from SNS and the computational models of mathematical programming for discovering the user groups with max joint influential power are proposed. The empirical evaluation with real data extracted from social networking sites shows that the proposed method can effectively identify the most influential groups when compared to the benchmark methods. This study opens the door to effectively conducting targeted marketing and enterprise reputation management on social networking sites. In complex networks, identifying influential nodes is the very important part of reliability analysis, which has been a key issue in analyzing the structural organization of a network. In this paper, a new evaluation method of node importance in complex networks based on technique for order performance by similarity to ideal solution (TOPSIS) approach is proposed. TOPSIS as a multiple attribute decision making (MADM) technique has been an important branch of decision making since then. In addition, TOPSIS is first applied to identify influential nodes in a complex network in this open issue. In different types of networks in which the information goes by different ways, we consider several different centrality measures as the multi-attribute of complex network in TOPSIS application. TOPSIS is utilized to aggregate the multi-attribute to obtain the evaluation of node importance of each node. It is not limited to only one centrality measure, but considers different centrality measures, because every centrality measure has its own disadvantage and limitation. Then, we use the Susceptible–Infected (SI) model to evaluate the performance. Numerical examples are given to show the efficiency and practicability of the proposed method. The target of viral marketing on the platform of popular online social networks is to rapidly propagate marketing information at lower cost and increase sales, in which a key problem is how to precisely discover the most influential users in the process of information diffusion. A novel method is proposed in this paper for helping companies to identify such users as seeds to maximize information diffusion in the viral marketing. Firstly, the user trust network oriented to viral marketing and users’ combined interest degree in the network including isolated users are extensively defined. Next, we construct a model considering the time factor to simulate the process of information diffusion in viral marketing and propose a dynamic algorithm description. Finally, experiments are conducted with a real dataset extracted from the famous SNS website Epinions. The experimental results indicate that the proposed algorithm has better scalability and is less time-consuming. Compared with the classical model, the proposed algorithm achieved a better performance than does the classical method on the two aspects of network coverage rate and time-consumption in our four sub-datasets. In large social networks, nodes (users, entities) are influenced by others for various reasons. For example, the colleagues have strong influence on one's work, while the friends have strong influence on one's daily life. How to differentiate the social influences from different angles(topics)? How to quantify the strength of those social influences? How to estimate the model on real large networks? To address these fundamental questions, we propose Topical Affinity Propagation (TAP) to model the topic-level social influence on large networks. In particular, TAP can take results of any topic modeling and the existing network structure to perform topic-level influence propagation. With the help of the influence analysis, we present several important applications on real data sets such as 1) what are the representative nodes on a given topic? 2) how to identify the social influences of neighboring nodes on a particular node? To scale to real large networks, TAP is designed with efficient distributed learning algorithms that is implemented and tested under the Map-Reduce framework. We further present the common characteristics of distributed learning algorithms for Map-Reduce. Finally, we demonstrate the effectiveness and efficiency of TAP on real large data sets.