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32
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.
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 maximization: Nearoptimal time complexity meets practical efficiency
 in SIGMOD. ACM
, 2014
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BaezaYates. Online topicaware influence maximization queries
 In EDBT
, 2014
"... Influence maximization is the key algorithmic problem behind viral marketing: it requires to identify a set of influential users in a social network, who, when convinced to adopt a product, shall influence other users in the network, leading to a large number of adoptions. Although real world user ..."
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Cited by 8 (0 self)
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Influence maximization is the key algorithmic problem behind viral marketing: it requires to identify a set of influential users in a social network, who, when convinced to adopt a product, shall influence other users in the network, leading to a large number of adoptions. Although real world users evidently have di↵erent degrees of interest and authoritativeness on di↵erent topics, the bulk of the literature on influence maximization is topicblind, in the sense that it treats all items as they were the same. In this paper we study Topicaware Influence Maximization (TIM) queries: given a directed social graph, where the arcs are associated with a topicdependent usertouser social influence strength, and given a budget k, the problem requires to find a set of k users (named seed set) that we shall
Profit Maximization over Social Networks
, 2012
"... Influence maximization is the problem of finding a set of influential users in a social network such that the expected spread of influence under a certain propagation model is maximized. Much of the previous work has neglected the important distinction between social influence and actual product ado ..."
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Influence maximization is the problem of finding a set of influential users in a social network such that the expected spread of influence under a certain propagation model is maximized. Much of the previous work has neglected the important distinction between social influence and actual product adoption. However, as recognized in the management science literature, an individual who gets influenced by social acquaintances may not necessarily adopt a product (or technology), due, e.g., to monetary concerns. In this work, we distinguish between influence and adoption by explicitly modeling the states of being influenced and of adopting a product. We extend the classical Linear Threshold (LT) model to incorporate prices and valuations, and factor them into users ’ decisionmaking process of adopting a product. We show that the expected profit function under our proposed model maintains submodularity under certain conditions, but no longer exhibits monotonicity, unlike the expected influence spread function. To maximize the expected profit under our extended LT model, we employ an unbudgeted greedy framework to propose three profit maximization algorithms. The results of our detailed experimental study on three realworld datasets demonstrate that of the three algorithms, PAGE, which assigns prices dynamically based on the profit potential of each candidate seed, has the best performance both in the expected profit achieved and in running time.
Core Decomposition of Uncertain Graphs
"... Core decomposition has proven to be a useful primitive for a wide range of graph analyses. One of its most appealing features is that, unlike other notions of dense subgraphs, it can be computed linearly in the size of the input graph. In this paper we provide an analogous tool for uncertain graphs, ..."
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Core decomposition has proven to be a useful primitive for a wide range of graph analyses. One of its most appealing features is that, unlike other notions of dense subgraphs, it can be computed linearly in the size of the input graph. In this paper we provide an analogous tool for uncertain graphs, i.e., graphs whose edges are assigned a probability of existence. The fact that core decomposition can be computed efficiently in deterministic graphs does not guarantee efficiency in uncertain graphs, where even the simplest graph operations may become computationally intensive. Here we show that core decomposition of uncertain graphs can be carried out efficiently as well. We extensively evaluate our definitions and methods on a number of realworld datasets and applications, such as influence maximization and taskdriven team formation.
Influence Spread in LargeScale Social Networks – A Belief Propagation Approach
"... Abstract. Influence maximization is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under a certain diffusion model. The Greedy algorithm for influence maximization first proposed by Kempe, later improved by Leskovec suffers from two source ..."
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Abstract. Influence maximization is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under a certain diffusion model. The Greedy algorithm for influence maximization first proposed by Kempe, later improved by Leskovec suffers from two sources of computational deficiency: 1) the need to evaluate many candidate nodes before selecting a new seed in each round, and 2) the calculation of the influence spread of any seed set relies on MonteCarlo simulations. In this work, we tackle both problems by devising efficient algorithms to compute influence spread and determine the best candidate for seed selection. The fundamental insight behind the proposed algorithms is the linkage between influence spread determination and belief propagation on a directed acyclic graph (DAG). Experiments using realworld social network graphs with scales ranging from thousands to millions of edges demonstrate the superior performance of the proposed algorithms with moderate computation costs. 1
Mining chains of relations ⋆
"... Abstract. Traditional data mining methods consider the problem of mining a single relation that relates two different attributes. For example, in a scientific bibliography database, authors are related to papers, and we may be interested in discovering association rules between authors based on the ..."
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Abstract. Traditional data mining methods consider the problem of mining a single relation that relates two different attributes. For example, in a scientific bibliography database, authors are related to papers, and we may be interested in discovering association rules between authors based on the papers that they have coauthored. However, in real life it is often the case that we have multiple attributes related through chains of relations. For example, authors write papers, and papers belong to one or more topics, defining a threelevel chain of relations. In this paper we consider the problem of mining such relational chains. We formulate a generic problem of finding selector sets (subsets of objects from one of the attributes) such that the projected dataset—the part of the dataset determined by the selector set—satisfies a specific property. The motivation for our approach is that a given property might not hold on the whole dataset, but holds when projecting the data on a subset of objects. We show that many existing and new data mining problems can be formulated in the framework. We discuss various algorithms and identify the conditions when apriori technique can be used. We experimentally demonstrate the effectiveness and efficiency of our methods. 1
Fast and accurate influence maximization on large networks with pruned montecarlo simulations
 In Conference on Artificial Intelligence (AAAI
, 2014
"... Influence maximization is a problem to find small sets of highly influential individuals in a social network to maximize the spread of influence under stochastic cascade models of propagation. Although the problem has been wellstudied, it is still highly challenging to find solutions of high qualit ..."
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Influence maximization is a problem to find small sets of highly influential individuals in a social network to maximize the spread of influence under stochastic cascade models of propagation. Although the problem has been wellstudied, it is still highly challenging to find solutions of high quality in largescale networks of the day. While MonteCarlosimulationbased methods produce nearoptimal solutions with a theoretical guarantee, they are prohibitively slow for large graphs. As a result, many heuristic methods without any theoretical guarantee have been developed, but all of them substantially compromise solution quality. To address this issue, we propose a new method for the influence maximization problem. Unlike other recent heuristic methods, the proposed method is a MonteCarlosimulationbased method, and thus it consistently produces solutions of high quality with the theoretical guarantee. On the other hand, unlike other previous MonteCarlosimulationbased methods, it runs as fast as other stateoftheart methods, and can be applied to large networks of the day. Through our extensive experiments, we demonstrate the scalability and the solution quality of the proposed method.
Online influence maximization (extended version). arXiv:1056.01188
, 2015
"... Social networks are commonly used for marketing purposes. For example, free samples of a product can be given to a few influential social network users (or “seed nodes”), with the hope that they will convince their friends to buy it. One way to formalize marketers ’ objective is through influence ma ..."
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Social networks are commonly used for marketing purposes. For example, free samples of a product can be given to a few influential social network users (or “seed nodes”), with the hope that they will convince their friends to buy it. One way to formalize marketers ’ objective is through influence maximization (or IM), whose goal is to find the best seed nodes to activate under a fixed budget, so that the number of people who get influenced in the end is maximized. Recent solutions to IM rely on the influence probability that a user influences another one. However, this probability information may be unavailable or incomplete. In this paper, we study IM in the absence of complete information on influence probability. We call this problem Online Influence Maximization (OIM) since we learn influence probabilities at the same time we run influence campaigns. To solve OIM, we propose a multipletrial approach, where (1) some seed nodes are selected based on existing influence information; (2) an influence campaign is started with these seed nodes; and (3) users ’ feedback is used to update influence information. We adopt the Explore–Exploit strategy, which can select seed nodes using either the current influence probability estimation (exploit), or the confidence bound on the estimation (explore). Any existing IM algorithm can be used in this framework. We also develop an incremental algorithm that can significantly reduce the overhead of handling users ’ feedback information. Our experiments show that our solution is more effective than traditional IM methods on the partial information. 1.