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Simpath: An efficient algorithm for influence maximization under the linear threshold model
 In Data Mining (ICDM), 2011 IEEE 11th International Conference on
, 2011
"... Abstract—There is significant current interest in the problem of influence maximization: given a directed social network with influence weights on edges and a number k, find k seed nodes such that activating them leads to the maximum expected number of activated nodes, according to a propagation mod ..."
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Abstract—There is significant current interest in the problem of influence maximization: given a directed social network with influence weights on edges and a number k, find k seed nodes such that activating them leads to the maximum expected number of activated nodes, according to a propagation model. Kempe et al. [1] showed, among other things, that under the Linear Threshold model, the problem is NPhard, and that a simple greedy algorithm guarantees the best possible approximation factor in PTIME. However, this algorithm suffers from various major performance drawbacks. In this paper, we propose SIMPATH, an efficient and effective algorithm for influence maximization under the linear threshold model that addresses these drawbacks by incorporating several clever optimizations. Through a comprehensive performance study on four real data sets, we show that SIMPATH consistently outperforms the state of the art w.r.t. running time, memory consumption and the quality of the seed set chosen, measured in terms of expected influence spread achieved.
Spotting Culprits in Epidemics: How many and Which ones
 in Proceedings of the 12th IEEE ICDM
, 2012
"... Abstract—Given a snapshot of a large graph, in which an infection has been spreading for some time, can we identify those nodes from which the infection started to spread? In other words, can we reliably tell who the culprits are? In this paper we answer this question affirmatively, and give an effi ..."
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Abstract—Given a snapshot of a large graph, in which an infection has been spreading for some time, can we identify those nodes from which the infection started to spread? In other words, can we reliably tell who the culprits are? In this paper we answer this question affirmatively, and give an efficient method called NETSLEUTH for the wellknown SusceptibleInfected virus propagation model. Essentially, we are after that set of seed nodes that best explain the given snapshot. We propose to employ the Minimum Description Length principle to identify the best set of seed nodes and virus propagation ripple, as the one by which we can most succinctly describe the infected graph. We give an highly efficient algorithm to identify likely sets of seed nodes given a snapshot. Then, given these seed nodes, we show we can optimize the virus propagation ripple in a principled way by maximizing likelihood. With all three combined, NETSLEUTH can automatically identify the correct number of seed nodes, as well as which nodes are the culprits. Experimentation on our method shows high accuracy in the detection of seed nodes, in addition to the correct automatic identification of their number. Moreover, we show NETSLEUTH scales linearly in the number of nodes of the graph. Keywordsculprits; epidemics; diffusion; seeds; I.
Security games for controlling contagion
 In TwentySixth National Conference in Artificial Intelligence
, 2012
"... Many strategic actions carry a ‘contagious ’ component beyond the immediate locale of the effort itself. Viral marketing and peacekeeping operations have both been observed to have a spreading effect. In this work, we use counterinsurgency as our illustrative domain. Defined as the effort to block ..."
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Cited by 16 (5 self)
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Many strategic actions carry a ‘contagious ’ component beyond the immediate locale of the effort itself. Viral marketing and peacekeeping operations have both been observed to have a spreading effect. In this work, we use counterinsurgency as our illustrative domain. Defined as the effort to block the spread of support for an insurgency, such operations lack the manpower to defend the entire population and must focus on the opinions of a subset of local leaders. As past researchers of security resource allocation have done, we propose using game theory to develop such policies and model the interconnected network of leaders as a graph. Unlike this past work in security games, actions in these domains possess a probabilistic, nonlocal impact. To address this new class of security games, we combine recent research in influence blocking maximization with a double oracle approach and create novel heuristic oracles to generate mixed strategies for a realworld leadership network from Afghanistan, synthetic leadership networks, and a real social network. We find that leadership networks that exhibit highly interconnected clusters can be solved equally well by our heuristic methods, but our more sophisticated heuristics outperform simpler ones in less interconnected social networks.
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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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
IRIE: Scalable and Robust Influence Maximization in Social Networks
"... Abstract—Influence maximization is the problem of selecting top k seed nodes in a social network to maximize their influence coverage under certain influence diffusion models. In this paper, we propose a novel algorithm IRIE that integrates the advantages of influence ranking (IR) and influence esti ..."
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Abstract—Influence maximization is the problem of selecting top k seed nodes in a social network to maximize their influence coverage under certain influence diffusion models. In this paper, we propose a novel algorithm IRIE that integrates the advantages of influence ranking (IR) and influence estimation (IE) methods for influence maximization in both the independent cascade (IC) model and its extension ICN that incorporates negative opinion propagations. Through extensive experiments, we demonstrate that IRIE matches the influence coverage of other algorithms while scales much better than all other algorithms. Moreover IRIE is much more robust and stable than other algorithms both in running time and memory usage for various density of networks and cascade size. It runs up to two orders of magnitude faster than other stateoftheart algorithms such as PMIA for large networks with tens of millions of nodes and edges, while using only a fraction of memory. Keywordssocial network mining, social network analysis, influence maximization, independent cascade model, viral marketing I.
Influence Diffusion Dynamics and Influence Maximization in Social Networks with Friend and Foe Relationships
, 2013
"... Influence diffusion and influence maximization in largescale online social networks (OSNs) have been extensively studied because of their impacts on enabling effective online viral marketing. Existing studies focus on social networks with only friendship relations, whereas the foe or enemy relation ..."
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Cited by 12 (2 self)
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Influence diffusion and influence maximization in largescale online social networks (OSNs) have been extensively studied because of their impacts on enabling effective online viral marketing. Existing studies focus on social networks with only friendship relations, whereas the foe or enemy relations that commonly exist in many OSNs, e.g., Epinions and Slashdot, are completely ignored. In this paper, we make the first attempt to investigate the influence diffusion and influence maximization in OSNs with both friend and foe relations, which are modeled using positive and negative edges on signed networks. In particular, we extend the classic voter model to signed networks and analyze the dynamics of influence diffusion of two opposite opinions. We first provide systematic characterization of both shortterm and longterm dynamics of influence diffusion in this model, and illustrate that the steady state behaviors of the dynamics depend on three types of graph structures, which we refer to as balanced graphs, antibalanced graphs, and strictly unbalanced graphs. We then apply our results to solve the influence maximization problem and develop efficient algorithms to select initial seeds of one opinion that maximize either its shortterm influence coverage or longterm steady state influence coverage. Extensive simulation results on both synthetic and realworld networks, such as Epinions and Slashdot, confirm our theoretical analysis on influence diffusion dynamics, and demonstrate that our influence maximization algorithms perform consistently better than other heuristic algorithms.
Influence maximization: Nearoptimal time complexity meets practical efficiency
 in SIGMOD. ACM
, 2014
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Influence Propagation in Social Networks: A Data Mining Perspective
, 2011
"... With the success of online social networks and microblogs such as Facebook, Flickr and Twitter, the phenomenon of influence exerted by users of such platforms on other users, and how it propagates in the network, has recently attracted the interest of computer scientists, information technologists, ..."
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Cited by 11 (2 self)
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With the success of online social networks and microblogs such as Facebook, Flickr and Twitter, the phenomenon of influence exerted by users of such platforms on other users, and how it propagates in the network, has recently attracted the interest of computer scientists, information technologists, and marketing specialists. One of the key problems in this area is the identification of influential users, by targeting whom certain desirable marketing outcomes can be achieved. In this article we take a data mining perspective and we discuss what (and how) can be learned from the available traces of past propagations. While doing this we provide a brief overview of some recent progresses in this area and discuss some open problems. By no means this article must be intended as an exhaustive survey: it is instead (admittedly) a rather biased and personal perspective of the author on the topic of influence propagation in social networks.