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Limiting the Spread of Misinformation in Social Networks
"... In this work, we study the notion of competing campaigns in a social network. By modeling the spread of influence in the presence of competing campaigns, we provide necessary tools for applications such as emergency response where the goal is to limit the spread of misinformation. We study the probl ..."
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In this work, we study the notion of competing campaigns in a social network. By modeling the spread of influence in the presence of competing campaigns, we provide necessary tools for applications such as emergency response where the goal is to limit the spread of misinformation. We study the problem of influence limitation where a “bad ” campaign starts propagating from a certain node in the network and use the notion of limiting campaigns to counteract the effect of misinformation. The problem can be summarized as identifying a subset of individuals that need to be convinced to adopt the competing (or “good”) campaign so as to minimize the number of people that adopt the “bad ” campaign at the end of both propagation processes. We show that this optimization problem is NPhard and provide approximation guarantees for a greedy solution for various definitions of this problem by proving that they are submodular. Although the greedy algorithm is a polynomial time algorithm, for today’s large scale social networks even this solution is computationally very expensive. Therefore, we study the performance of the degree centrality heuristic as well as other heuristics that have implications on our specific problem. The experiments on a number of closeknit regional networks obtained from the Facebook social network show that in most cases inexpensive heuristics do in fact compare well with the greedy approach.
TimeCritical Influence Maximization in Social Networks with TimeDelayed Diffusion Process
"... Influence maximization is a problem of finding a small set of highly influential users in a social network such that the spread of influence under certain propagation models is maximized. In this paper, we consider timecritical influence maximization, in which one wants to maximize influence spread ..."
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Cited by 16 (1 self)
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Influence maximization is a problem of finding a small set of highly influential users in a social network such that the spread of influence under certain propagation models is maximized. In this paper, we consider timecritical influence maximization, in which one wants to maximize influence spread within a given deadline. Since timing is considered in the optimization, we also extend the Independent Cascade (IC) model to incorporate the time delay aspect of influence diffusion in social networks. We show that timecritical influence maximization under the timedelayed IC model maintains desired properties such as submodularity, which allows a greedy algorithm to achieve an approximation ratio of 1 − 1/e, to circumvent the NPhardness of the problem. To overcome the inefficiency of the approximation algorithm, we design two heuristic algorithms: the first one is based on a dynamic programming procedure that computes exact influence in tree structures, while the second one converts the problem to one in the original IC model and then applies existing fast heuristics to it. Our simulation results demonstrate that our heuristics achieve the same level of influence spread as the greedy algorithm while running a few orders of magnitude faster, and they also outperform existing algorithms that disregard the deadline constraint and delays in diffusion. 1
A distributed and privacy preserving algorithm for identifying information hubs in social networks
 In 27 INFOCOM 2011 Proceedings, pages 561 – 565
, 2011
"... Abstract—This paper addresses the problem of identifying the topk information hubs in a social network. Identifying topk information hubs is crucial for many applications such as advertising in social networks where advertisers are interested in identifying hubs to whom free samples can be given. E ..."
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Abstract—This paper addresses the problem of identifying the topk information hubs in a social network. Identifying topk information hubs is crucial for many applications such as advertising in social networks where advertisers are interested in identifying hubs to whom free samples can be given. Existing solutions are centralized and require time stamped information about pairwise user interactions and can only be used by social network owners as only they have access to such data. Existing distributed and privacy preserving algorithms suffer from poor accuracy. In this paper, we propose a new algorithm to identify information hubs that preserves user privacy. The intuition is that highly connected users tend to have more interactions with their neighbors than less connected users. Our method can identify hubs without requiring a central entity to access the complete friendship graph. We achieve this by
Learning Diffusion Probability based on Node Attributes in Social Networks
"... Abstract. Information diffusion over a social network is analyzed by modeling the successive interactions of neighboring nodes as probabilistic processes of state changes. We address the problem of estimating parameters (diffusion probability and timedelay parameter) of the probabilistic model as ..."
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Abstract. Information diffusion over a social network is analyzed by modeling the successive interactions of neighboring nodes as probabilistic processes of state changes. We address the problem of estimating parameters (diffusion probability and timedelay parameter) of the probabilistic model as a function of the node attributes from the observed diffusion data by formulating it as the maximum likelihood problem. We show that the parameters are obtained by an iterative updating algorithm which is efficient and is guaranteed to converge. We tested the performance of the learning algorithm on three real world networks assuming the attribute dependency, and confirmed that the dependency can be correctly learned. We further show that the influence degree of each node based on the linkdependent diffusion probabilities is substantially different from that obtained assuming a uniform diffusion probability which is approximated by the average of the true linkdependent diffusion probabilities. 1
Identifying influential agents for advertising in multiagent markets
 in Proceedings of the International Conference on Autonomous Agents and Multiagent Systems
"... The question of how to influence people in a large social system is a perennial problem in marketing, politics, and publishing. It differs from more personal interagent interactions that occur in negotiation and argumentation since network structure and group membership often pay a more significant ..."
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The question of how to influence people in a large social system is a perennial problem in marketing, politics, and publishing. It differs from more personal interagent interactions that occur in negotiation and argumentation since network structure and group membership often pay a more significant role than the content of what is being said, making the messenger more important than the message. In this paper, we propose a new method for propagating information through a social system and demonstrate how it can be used to develop a product advertisement strategy in a simulated market. We consider the desire of agents toward purchasing an item as a random variable and solve the influence maximization problem in steady state using an optimization method to assign the advertisement of available products to appropriate messenger agents. Our market simulation accounts for the 1) effects of group membership on agent attitudes 2) has a network structure that is similar to realistic human systems 3) models interproduct preference correlations that can be learned from market data. The results show that our method is significantly better than network analysis methods based on centrality measures.
New insights from an analysis of social influence networks under the linear threshold model,” http://arxiv.org/abs/1002.1335
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Evolutionary Dynamics of Information Diffusion Over Social Networks
"... Abstract—Current social networks are of extremely largescale generating tremendous information flows at every moment. How information diffuses over social networks has attracted much attention from both industry and academics. Most of the existing works on information diffusion analysis are based o ..."
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Abstract—Current social networks are of extremely largescale generating tremendous information flows at every moment. How information diffuses over social networks has attracted much attention from both industry and academics. Most of the existing works on information diffusion analysis are based on machine learning methods focusing on social network structure analysis and empirical data mining. However, the network users ’ decisions, actions, and socioeconomic interactions are generally ignored by most of existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic information diffusion process in social networks. Specifically, we derive the information diffusion dynamics in complete networks, uniform degree, and nonuniform degree networks, with the highlight of two special networks, the Erdős–Rényi random network and the Barabási–Albert scalefree network. We find that the dynamics of information diffusion over these three kinds of networks are scalefree and all the three dynamics are same with each other when the network scale is sufficiently large. To verify our theoretical analysis, we perform simulations for the information diffusion over synthetic networks and realworld Facebook networks. Moreover, we also conduct an experiment on a Twitter hashtags dataset, which shows that the proposed game theoretic model can well fit and predict the information diffusion over real social networks. Index Terms—Evolutionary game, game theory, information diffusion, information spreading, social networks. I.
Discovery of supermediators of information diffusion in social networks
 In Proceedings of the 13th international conference on Discovery science, DS’10
, 2010
"... Abstract. We address the problem of discovering a different kind of influential nodes, which we call ”supermediator”, i.e. those nodes which play an important role to pass the information to other nodes, and propose a method for discovering supermediators from information diffusion samples withou ..."
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Abstract. We address the problem of discovering a different kind of influential nodes, which we call ”supermediator”, i.e. those nodes which play an important role to pass the information to other nodes, and propose a method for discovering supermediators from information diffusion samples without using a network structure. We divide the diffusion sequences in two groups (lower and upper), each assuming some probability distribution, find the best split by maximizing the likelihood, and rank the nodes in the upper sequences by the Fmeasure. We apply this measure to the information diffusion samples generated by two real networks, identify and rank the supermediator nodes. We show that the high ranked supermediators are also the high ranked influential nodes when the diffusion probability is large, i.e. the influential nodes also play a role of supermediator for the other source nodes, and interestingly enough that when the high ranked supermediators are different from the top ranked influential nodes, which is the case when the diffusion probability is small, those supermediators become the high ranked influential nodes when the diffusion probability becomes larger. This finding will be useful to predict the influential nodes for the unexperienced spread of new information, e.g. spread of new acute contagion. 1
Efficient estimation of influence functions for sis model on social networks
 In Proc. IJCAI’09
, 2009
"... Abstract We address the problem of efficiently estimating the influence function of initially activated nodes in a social network under the susceptible/infected/susceptible (SIS) model, a diffusion model where nodes are allowed to be activated multiple times. The computational complexity drasticall ..."
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Abstract We address the problem of efficiently estimating the influence function of initially activated nodes in a social network under the susceptible/infected/susceptible (SIS) model, a diffusion model where nodes are allowed to be activated multiple times. The computational complexity drastically increases because of this multiple activation property. We solve this problem by constructing a layered graph from the original social network with each layer added on top as the time proceeds, and applying the bond percolation with a pruning strategy. We show that the computational complexity of the proposed method is much smaller than the conventional naive probabilistic simulation method by a theoretical analysis and confirm this by applying the proposed method to two real world networks.
An Effective Method of Discovering Target Groups on Social Networking Sites Completed Research Paper
"... With the popularity of social networking sites (SNS) in this era of Web 2.0, increasingly more users are contributing their opinions about products and organizations. These online comments often have direct influence on consumers ’ buying decisions and the public’s impressions of enterprises. As a r ..."
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With the popularity of social networking sites (SNS) in this era of Web 2.0, increasingly more users are contributing their opinions about products and organizations. 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 use SNS to conduct targeted marking and reputation management. As indicated from recent marketing research, the joint influence power of a small group of active users could have considerable impact on consumers ’ buying decisions and the public’s perception of the enterprises. To help enterprises conduct costeffective targeted marketing and reputation management, this paper illustrates a novel methodology that can effectively discover the most influential users from SNS. In particular, the general methodology of mining the influence network from SNS and the computational models of mathematical programming for discovering the user groups with the maximal joint influence power are proposed. The empirical evaluation with real data extracted from SNS shows that the proposed method can effectively identify the most influential groups when compared