Results 1  10
of
28
Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model
"... In many realworld situations, different and often opposite opinions, innovations, or products are competing with one another for their social influence in a networked society. In this paper, we study competitive influence propagation in social networks under the competitive linear threshold (CLT) m ..."
Abstract

Cited by 25 (5 self)
 Add to MetaCart
(Show Context)
In many realworld situations, different and often opposite opinions, innovations, or products are competing with one another for their social influence in a networked society. In this paper, we study competitive influence propagation in social networks under the competitive linear threshold (CLT) model, an extension to the classic linear threshold model. Under the CLT model, we focus on the problem that one entity tries to block the influence propagation of its competing entity as much as possible by strategically selecting a number of seed nodes that could initiate its own influence propagation. We call this problem the influence blocking maximization (IBM) problem. We prove that the objective function of IBM in the CLT model is submodular, and thus a greedy algorithm could achieve 1−1/e approximation ratio. However, the greedy algorithm requires MonteCarlo simulations of competitive influence propagation, which makes the algorithm not efficient. We design an efficient algorithm CLDAG, which utilizes the properties of the CLT model, to address this issue. We conduct extensive simulations of CLDAG, the greedy algorithm, and other baseline algorithms on realworld and synthetic datasets. Our results show that CLDAG is able to provide best accuracy in par with the greedy algorithm and often better than other algorithms, while it is two orders of magnitude faster than the greedy algorithm.
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 ..."
Abstract

Cited by 16 (1 self)
 Add to MetaCart
(Show Context)
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 ..."
Abstract

Cited by 13 (1 self)
 Add to MetaCart
(Show Context)
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 ..."
Abstract

Cited by 12 (2 self)
 Add to MetaCart
(Show Context)
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.
Maximizing Product Adoption in Social Networks
"... One of the key objectives of viral marketing is to identify a small set of users in a social network, who when convinced to adopt a product will influence others in the network leading to a large number of adoptions in an expected sense. The seminal work of Kempe et al. [13] approaches this as the p ..."
Abstract

Cited by 11 (2 self)
 Add to MetaCart
(Show Context)
One of the key objectives of viral marketing is to identify a small set of users in a social network, who when convinced to adopt a product will influence others in the network leading to a large number of adoptions in an expected sense. The seminal work of Kempe et al. [13] approaches this as the problem of influence maximization. This and other previous papers tacitly assume that a user who is influenced (or, informed) about a product necessarily adopts the product and encourages her friends to adopt it. However, an influenced user may not adopt the product herself, and yet form an opinion based on the experiences of her friends, and share this opinion with others. Furthermore, a user who adopts the product may not like it and hence not encourage her friends to adopt it to the same extent as another user who adopted and liked the product. This is independent of the extent to which those friends are influenced by her. Previous works do not account for these phenomena. We argue that it is important to distinguish product adoption from influence. We propose a model that factors in a user’s experience (or projected experience) with a product. We adapt the classical Linear Threshold (LT) propagation model by defining an objective function that explicitly captures product adoption, as opposed to influence. We show that under our model, adoption maximization is NPhard and the objective function is monotone and submodular, thus admitting an approximation algorithm. We perform experiments on three real popular social networks and show that our model is able to distinguish between influence and adoption, and predict product adoption much more accurately than the classical LT model.
CINEMA: ConformityAware Greedy Algorithm for Influence Maximization in Online Social Networks
, 2013
"... Influence maximization (im) is the problem of finding a small subset of nodes (seed nodes) in a social network that could maximize the spread of influence. Despite the progress achieved by stateoftheart greedyim techniques, they suffer from two key limitations. Firstly, they are inefficient as th ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
(Show Context)
Influence maximization (im) is the problem of finding a small subset of nodes (seed nodes) in a social network that could maximize the spread of influence. Despite the progress achieved by stateoftheart greedyim techniques, they suffer from two key limitations. Firstly, they are inefficient as they can take days to find seeds in very large realworld networks. Secondly, although extensive research in social psychology suggests that humans will readily conform to the wishes or beliefs of others, surprisingly, existingim techniques are conformityunaware. That is, they only utilize an individual’s ability to influence another but ignores conformity (a person’s inclination to be influenced) of the individuals. In this paper, we propose a novel conformityaware cascade (c 2) model which leverages on the interplay between influence and conformity in obtaining the influence probabilities of nodes from underlying
Emerging Graph Queries In Linked Data
"... Abstract—In a wide array of disciplines, data can be modeled as an interconnected network of entities, where various attributes could be associated with both the entities and the relations among them. Knowledge is often hidden in the complex structure and attributes inside these networks. While que ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
(Show Context)
Abstract—In a wide array of disciplines, data can be modeled as an interconnected network of entities, where various attributes could be associated with both the entities and the relations among them. Knowledge is often hidden in the complex structure and attributes inside these networks. While querying and mining these linked datasets are essential for various applications, traditional graph queries may not be able to capture the rich semantics in these networks. With the advent of complex information networks, new graph queries are emerging, including graph pattern matching and mining, similarity search, ranking and expert finding, graph aggregation and OLAP. These queries require both the topology and content information of the network data, and hence, different from classical graph algorithms such as shortest path, reachability and minimum cut, which depend only on the structure of the network. In this tutorial, we shall give an introduction of the emerging graph queries, their indexing and resolution techniques, the current challenges and the future research directions. I.
Predicting Information Diffusion on Social Networks with Partial Knowledge
"... Models of information diffusion and propagation over large social media usually rely on a Close World Assumption: information can only propagate onto the network relational structure, it cannot come from external sources, the network structure is supposed fully known by the model. These assumptions ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
(Show Context)
Models of information diffusion and propagation over large social media usually rely on a Close World Assumption: information can only propagate onto the network relational structure, it cannot come from external sources, the network structure is supposed fully known by the model. These assumptions are nonrealistic for many propagation processes extracted from Social Websites. We address the problem of predicting information propagation when the network diffusion structure is unknown and without making any closed world assumption. Instead of modeling a diffusion process, we propose to directly predict the final propagation state of the information over a whole user set. We describe a general model, able to learn predicting which users are the most likely to be contaminated by the information knowing an initial state of the network. Different instances are proposed and evaluated on artificial datasets.
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 ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
(Show Context)
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
Diffusion of “following” links in microblogging networks
 IEEE Transaction on Knowledge and Data Engineering (TKDE
, 2015
"... Abstract—When a “following ” link is formed in a social network, will the link trigger the formation of other neighboring links? We study the diffusion phenomenon of the formation of “following ” links by proposing a model to describe this link diffusion process. To estimate the diffusion strength b ..."
Abstract

Cited by 2 (2 self)
 Add to MetaCart
(Show Context)
Abstract—When a “following ” link is formed in a social network, will the link trigger the formation of other neighboring links? We study the diffusion phenomenon of the formation of “following ” links by proposing a model to describe this link diffusion process. To estimate the diffusion strength between different links, we first conduct an analysis on the diffusion effect in 24 triadic structures and find evident patterns that facilitate the effect. We then learn the diffusion strength in different triadic structures by maximizing an objective function based on the proposed model. The learned diffusion strength is evaluated through the task of link prediction and utilized to improve the applications of follower maximization and followee recommendation, which are specific instances of influence maximization. Our experimental results reveal that incorporating diffusion patterns can indeed lead to statistically significant improvements over the performance of several alternative methods, which demonstrates the effect of the discovered patterns and diffusion model. Index Terms—Link diffusion, Triad formation, Social network F