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How to win friends and influence people, truthfully: Influence maximization mechanisms for social networks
 In WSDM
, 2012
"... Throughout the past decade there has been extensive research on algorithmic and data mining techniques for solving the problem of influence maximization in social networks: if one can incentivize a subset of individuals to become early adopters of a new technology, which subset should be selected so ..."
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Cited by 25 (3 self)
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Throughout the past decade there has been extensive research on algorithmic and data mining techniques for solving the problem of influence maximization in social networks: if one can incentivize a subset of individuals to become early adopters of a new technology, which subset should be selected so that the wordofmouth effect in the social network is maximized? Despite the progress in modeling and techniques, the incomplete information aspect of the problem has been largely overlooked. While data can often provide the network structure and influence patterns may be observable, the inherent cost individuals have to become early adopters is difficult to extract. In this paper we introduce mechanisms that elicit individuals’ costs while providing desirable approximation guarantees in some of the most wellstudied models of social network influence. We follow the mechanism design framework which advocates for allocation and payment schemes that incentivize individuals to report their true information. We also performed experiments using the Mechanical Turk platform and social network data to provide evidence of the framework’s effectiveness in practice.
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.
Fast Robustness Estimation in Large Social Graphs: Communities and Anomaly Detection
"... Given a large social graph, like a scientific collaboration network, what can we say about its robustness? Can we estimate a robustness index for a graph quickly? If the graph evolves over time, how these properties change? In this work, we are trying to answer the above questions studying the expan ..."
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Cited by 6 (1 self)
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Given a large social graph, like a scientific collaboration network, what can we say about its robustness? Can we estimate a robustness index for a graph quickly? If the graph evolves over time, how these properties change? In this work, we are trying to answer the above questions studying the expansion properties of large social graphs. First, we present a measure which characterizes the robustness properties of a graph, and serves as global measure of the community structure (or lack thereof). We study how these properties change over time and we show how to spot outliers and anomalies over time. We apply our method on several diverse real networks with millions of nodes. We also show how to compute our measure efficiently by exploiting the special spectral properties of realworld networks.
Influence Maximization in Social Networks: Towards an Optimal Algorithmic Solution
, 1212
"... Diffusion is a fundamental graph process, underpinning such phenomena as epidemic disease contagion and the spread of innovation by wordofmouth. We address the algorithmic problem of finding a set of k initial seed nodes in a network so that the expected size of the resulting cascade is maximized, ..."
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Cited by 6 (1 self)
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Diffusion is a fundamental graph process, underpinning such phenomena as epidemic disease contagion and the spread of innovation by wordofmouth. We address the algorithmic problem of finding a set of k initial seed nodes in a network so that the expected size of the resulting cascade is maximized, under the standard independent cascade model of network diffusion. The promiseofsuchanalgorithmliesinapplicationstoviralmarketing. However,runtimeisofcritical importance in this endeavor due to the massive size and volatility of the relevant networks. Our main result is an algorithm for the influence maximization problem that obtains the nearoptimal approximation factor of (1 − 1 e − ǫ), for any ǫ> 0, in time O((m + n)ǫ−3 logn) where n and m are the number of vertices and edges in the network. The runtime of our algorithm is independent of the number of seeds k and improves upon the previously bestknown algorithms whichrun in time Ω(mnk·POLY(ǫ −1)). Importantly, ouralgorithmis essentiallyruntimeoptimal (up to a logarithmic factor) as we establish a lower bound of Ω(m+n) on the runtime required to obtain a constant approximation.
Adaptive seeding in social networks
 IN FOCS
, 2013
"... The algorithmic challenge of maximizing information diffusion through wordofmouth processes in social networks has been heavily studied in the past decade. Despite immense progress and an impressive arsenal of techniques, the algorithmic framework makes idealized assumptions regarding access to ..."
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Cited by 4 (2 self)
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The algorithmic challenge of maximizing information diffusion through wordofmouth processes in social networks has been heavily studied in the past decade. Despite immense progress and an impressive arsenal of techniques, the algorithmic framework makes idealized assumptions regarding access to the network that can often result in poor performance of stateoftheart techniques. In this paper we introduce a new framework which we call Adaptive Seeding. The framework is a twostage stochastic optimization model designed to leverage the high potential that typically lies in neighboring nodes of arbitrary samples of social networks. Our main result is an algorithm which is a constant factor approximation of the optimal adaptive policy for any influence function in the Triggering model.
Finding Influential Seed Successors in Social Networks
, 2012
"... In a dynamic social network, nodes can be removed from the network for some reasons, and consequently affect the behaviors of the network. In this paper, we tackle the challenge of finding a successor node for each removed seed node to maintain the influence spread in the network. Given a social net ..."
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Cited by 2 (0 self)
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In a dynamic social network, nodes can be removed from the network for some reasons, and consequently affect the behaviors of the network. In this paper, we tackle the challenge of finding a successor node for each removed seed node to maintain the influence spread in the network. Given a social network and a set of seed nodes for influence maximization, the problem is to effectively choose successors to inherit the jobs of initial influence propagation when some seeds are removed from the network. To tackle this problem, we present and discuss five neighborhoodbased selection heuristics, including degree, degree discount, overlapping, community bridge, and community degree. Experiments on DBLP coauthorship network show the effectiveness of devised heuristics.
Fast Influencebased Coarsening for Large Networks
"... Given a social network, can we quickly ‘zoomout ’ of the graph? Is there a smaller equivalent representation of the graph that preserves its propagation characteristics? Can we group nodes together based on their influence properties? These are important problems with applications to influence anal ..."
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Given a social network, can we quickly ‘zoomout ’ of the graph? Is there a smaller equivalent representation of the graph that preserves its propagation characteristics? Can we group nodes together based on their influence properties? These are important problems with applications to influence analysis, epidemiology and viral marketing applications. In this paper, we first formulate a novel Graph Coarsening Problem to find a succinct representation of any graph while preserving key characteristics for diffusion processes on that graph. We then provide a fast and effective nearlineartime (in nodes and edges) algorithm coarseNet for the same. Using extensive experiments on multiple real datasets, we demonstrate the quality and scalability of coarseNet, enabling us to reduce the graph by 90 % in some cases without much loss of information. Finally we also show how our method can help in diverse applications like influence maximization and detecting patterns of propagation at the level of automatically created groups on real cascade data.
Approximability of Adaptive Seeding under Knapsack Constraints
, 2015
"... Adapting Seeding is a key algorithmic challenge of influence maximization in social networks. One seeks to select among certain available nodes in a network, and then, adaptively, among neighbors of those nodes as they become available, in order to maximize influence in the overall network. Despite ..."
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Adapting Seeding is a key algorithmic challenge of influence maximization in social networks. One seeks to select among certain available nodes in a network, and then, adaptively, among neighbors of those nodes as they become available, in order to maximize influence in the overall network. Despite recent strong approximation results [25, 1], very little is known about the problem when nodes can take on different activation costs. Surprisingly, designing adaptive seeding algorithms that can appropriately incentivize users with heterogeneous activation costs introduces fundamental challenges that do not exist in the simplified version of the problem. In this paper we study the approximability of adaptive seeding algorithms that incentivize nodes with heterogeneous activation costs. We first show a tight inapproximability result which applies even for a very restricted version of the problem. We then complement this inapproximability with a constantfactor approximation for general submodular functions, showing that the difficulties caused by the stochastic nature of the problem can be overcome. In addition, we show stronger approximation results for additive influence functions and cases where the nodes’ activation costs constitute a small fraction of the budget. 1
Influence Propagation in Adversarial Setting: How to Defeat Competition with Least Amount of Investment
"... It has been observed that individuals ’ decisions to adopt a product or innovation are often influenced by the recommendations of their friends and acquaintances. Motivated by this observation, the last few years have seen a number of studies on influence maximization in social networks. The primar ..."
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It has been observed that individuals ’ decisions to adopt a product or innovation are often influenced by the recommendations of their friends and acquaintances. Motivated by this observation, the last few years have seen a number of studies on influence maximization in social networks. The primary goal of these studies is identification of k most influential nodes in a network. A major limitation of these studies is that they focus on a nonadversarial environment, where only one player is engaged in influencing the nodes. However, in a realistic scenario multiple players attempt to influence the nodes in a competitive fashion. The proposed model considers a competitive environment where a node that has not yet adopted an innovation, can adopt only one of the several competing innovations and once it adopts an