Influence Propagation Model Research Articles

Finding top-k influential users in social networks under the structural diversity model

The influence maximization problem in a large-scale social network is to identify a few influential users such that their influence on the other users in the network is maximized, under a given influence propagation model. One common assumption adopted by two popular influence propagation models is that a user is more likely to be influenced if more his/her friends have already been influenced. This assumption recently however was challenged to be over simplified and inaccurate, as influence propagation process typically is much more complex than that, and the social decision of a user depends more subtly on the network structure, rather than how many his/her influenced friends. Instead, it has been shown that a user is very likely to be influenced by structural diversities of his/her friends. In this paper, we first formulate a novel influence maximization problem under this new structural diversity model. We then propose a constant approximation algorithm for the problem. We finally evaluate the effectiveness of the proposed algorithm by extensive experimental simulations, using different real datasets. Experimental results show that the users identified from a social network by the proposed algorithm have much larger influence than that found by existing algorithms.

A subjective evidence model for influence maximization in social networks

This paper introduces the notion of subjective evidence, which fuels a new parallel cascade influence propagation model. The model sheds light on the phenomena of belief reinforcement and viral spread of innovations, rumors, opinions, etc., in social networks. Network actors are assumed to be testing a Bayesian hypothesis, e.g., for making judgment about the superiority of some product(s) or service(s) over others, or (dis)utility of a given program/policy. The model-based influence maximization solutions inform the strategies for market niche selection and protection, and identification of susceptible groups in political campaigning. The NP-Hard problem of influential seed selection is first solved as a mixed-integer program. Second, an efficient Lagrangian Relaxation heuristic with guaranteed bounds is presented. In small, medium and large-scale computational investigations, we analyze: (1) how the success of an influence cascade triggered in a (sub)community, long exposed to an opposite belief, depends on the structural properties of the underlying social network, (2) to what extent growing (increasing the density of) a consumer network within a market niche helps a company protect the niche, (3) given a competitor׳s strength, when a company should counter the competitor on “their turf”, and when and how it should look for limited-time opportunities to maximally profit before eventually surrendering the market.

Analyzing competitive influence maximization problems with partial information: An approximation algorithmic framework

Given the popularity of the viral marketing campaign in online social networks, finding a computationally efficient method to identify a set of most influential nodes so as to compete well with others is of the utmost importance. In this paper, we propose a general model to describe the influence propagation of multiple competing sources in the same network. We formulate the Competitive Influence Maximization with Partial information (CIMP) problem: given an influence propagation model and the probability of a node being in the competitor’s seed set, how to find a set of k seeds so to trigger the largest expected influence cascade under the presence of other competitors? We propose a general algorithmic framework, Two-phase Competitive Influence Maximization (TCIM), to address the CIMP problem. TCIM returns a (1−1/e−ϵ)-approximate solution with probability of at least 1−n−ℓ, where ℓ≥1/2 is a parameter controlling the trade-off between the success probability and the computational efficiency. TCIM has an efficient expected time complexity of O(c(k+ℓ)(m+n)logn/ϵ2), where n and m are the number of nodes and edges in the network, and c is a function of the given propagation model (which may depend on k and the underlying network). To the best of our knowledge, this is the first work which provides a general framework for the competitive influence maximization problem where the seeds of the competitor could be given as an explicit set of seed nodes or a probability distribution of seed nodes. Moreover, our algorithmic framework provides both quality guarantee of solution and practical computational efficiency. We conduct extensive experiments on real-world datasets under three specific influence propagation models, and show the efficiency and accuracy of our framework. In particular, for the case where the seed set of the competitor is given explicitly, we achieve up to four orders of magnitude speedup as compared to previous algorithms with the same quality guarantee. When the competitor’s seed set is not given explicitly, running TCIM using the probability distribution of the competitor’s seeds returns nodes with higher expected influence than those nodes returned by TCIM using an explicit guess of the competitor’s seeds.

A model-independent approach for efficient influence maximization in social networks

The well-known influence maximization problem (Kempe et al., in proceedings of the 9th SIGKDD international conference on knowledge discovery and data mining (KDD), pp 137–146, 2003) (or viral marketing through social networks) deals with selecting a few influential initial seeds to maximize the awareness of product(s) over the social network. As it is computationally hard (Kempe et al., in proceedings of the 9th SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 137–146, 2003), a greedy approximation algorithm is designed to address the influence maximization problem. However, the major drawback of this greedy algorithm is that it runs extremely slow even on network datasets consisting of a few thousand nodes and edges (Leskovec et al., in proceedings of the 13th SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 420–429, 2007; Checn et al., in proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 937–944, 2009). Several efficient heuristics have been proposed in the literature (Checn et al., in proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 937–944, 2009) to alleviate this computational difficulty; however, these heuristics are designed for specific influence propagation models such as linear threshold model and independent cascade model. This motivates the strong need to design an approach that not only works with any influence propagation model, but also efficiently solves the influence maximization problem. In this paper, we precisely address this problem by proposing a new framework which fuses both link and interaction data to come up with a backbone for a given social network, which can further be used for efficient influence maximization. We then conduct thorough experimentation with several real-life social network datasets such as DBLP, Epinions, Digg, and Slashdot and show that the proposed approach is efficient as well as scalable.

Influence-based community partition for social networks

Background/PurposeCommunity partition is of great importance in sociology, biology and computer science. Due to the exponentially increasing amount of social network applications, a fast and accurate method is necessary for community partition in social networks. In view of this, we investigate the social community partition problem from the perspective of influence propagation, which is one of the most important features of social communication.MethodsWe formulate social community partition as a combinatorial optimization problem that aims at partitioning a social network into K disjoint communities such that the sum of influence propagation within each community is maximized. When K=2 we develop an optimal algorithm that has a provable performance guarantee for a class of influence propagation models. For general K, we prove that it is NP-hard to find a maximum partition for social networks in the well-known linear threshold and independent cascade models. To get near-optimal solutions, we develop a greedy algorithm based on the optimal algorithm. We also develop a heuristic algorithm with a low computational complexity for large social networks.ResultsTo evaluate the practical efficiency of our algorithms, we do a simulation study based on real world scenarios. The experiments are conducted on three real-world social networks, and the experimental results show that more accurate partitions according to influence propagation can be obtained using our algorithms rather than using some classic community partition algorithms.ConclusionsIn this study, we investigate the community partition problem in social networks. It is formulated as an optimization problem and investigated both theoretically and practically. The results can be applied to find communities in social networks and are also useful for the influence propagation problem in social networks.

A nature-inspired influence propagation model for the community expansion problem

Influence propagation has been widely studied in social networks recently. Most of these existing work mainly focuses on the individual influence or the seed set influence. However, a large range of real world applications are related with the influence from communities. In this paper, we argue that the specific structure of community makes the influence propagation from a community different from previous influence propagation from an individual or a seed set. Inspired by the charged system in the physic, a new community influence propagation model is built, which provides a natural description about the process of influence propagation and explains why the influence makes communities expand. Based on this physical model, we define the community expansion problem. And two objective functions are proposed for choosing proper candidates to enlarge a community, taking into account the cost and benefit. Then a linear programming approach is designed to maximize those two objective functions. To validate our ideas and algorithm, we construct experiments on three real-world networks. The results demonstrate that our model and algorithm are effective in choosing proper candidates for expanding a community, comparing to other two algorithms.

Influence maximization algorithm for micro-blog network

Influence maximization problem in micro-blog cannot be solved by simple user rank algorithm.To solve this problem,a greedy algorithm based on Extended Linear Threshold Model(ELTM) was proposed to solve Top-K problem in microblog.A concept of influence rate and a WIR(Weibo Influence Rank) algorithm were established to determine the user's influence by summarizing the key factors.Then,based on WIR values,an influence propagation model was proposed.After using greedy algorithm,the Top-K nodes were excavated.A simulation test based on Sina micro-blog was performed to validate the effectiveness of the proposed method.The result shows that the method outperforms the traditional algorithm.

Topic-aware social influence propagation models

The study of influence-driven propagations in social networks and its exploitation for viral marketing purposes has recently received a large deal of attention. However, regardless of the fact that users authoritativeness, expertise, trust and influence are evidently topic-dependent, the research on social influence has surprisingly largely overlooked this aspect. In this article, we study social influence from a topic modeling perspective. We introduce novel topic-aware influence-driven propagation models that, as we show in our experiments, are more accurate in describing real-world cascades than the standard (i.e., topic-blind) propagation models studied in the literature. In particular, we first propose simple topic-aware extensions of the well-known Independent Cascade and Linear Threshold models. However, these propagation models have a very large number of parameters which could lead to overfitting. Therefore, we propose a different approach explicitly modeling authoritativeness, influence and relevance under a topic-aware perspective. Instead of considering user-to-user influence, the proposed model focuses on user authoritativeness and interests in a topic, leading to a drastic reduction in the number of parameters of the model. We devise methods to learn the parameters of the models from a data set of past propagations. Our experimentation confirms the high accuracy of the proposed models and learning schemes.

Loyalty improvement beyond the seeds in social networks

The influence maximization problem in modular social networks is to find a set of seed nodes such that the total influence effect is maximized. Difference with the previous research, in this paper we propose a novel task of influence improving, which is to find strategies to increase the members' investments. The problem is studied under two influence propagation models: independent cascade (IC) and linear threshold (LT) models. We prove that our influence improving problem is $$\mathcal{NP }$$ NP -hard, and propose new algorithms under both IC and LT models. To the best of our knowledge, our work is the first one that studies influence improving problem under bounded budget. Finally, we implement extensive experiments over a large data collection obtained from real-world social networks, and evaluate the performance of our approach.

Design of viral marketing strategies for product cross-sell through social networks

In this paper, we introduce a novel and generalized version of the influence maximization problem in social networks, which we call as Budgeted Influence Maximization with Cross-sell of Products (B-IMCP), and it considers simultaneously the following three practical aspects: (i) Often cross-sell among products is possible, (ii) Product-specific costs (and benefits) for promoting the products have to be considered, and (iii) Since a company often has budget constraints, the initial seeds have to be chosen within a given budget. In particular, we consider that the cross-sell relationships among the products of a single company are given by an arbitrary bipartite graph. We explore two variants of cross-sell, one weak and one strong, and also assume product-specific costs and benefits. This leads to two different versions of the B-IMCP problem. Given a fixed budget, one of the key issues associated with each version of the B-IMCP problem is to choose the initial seeds within this budget not only for the individual products, but also for promoting cross-sell phenomenon among these products. The following are the specific contributions of this paper: (i) We propose a novel influence propagation model to capture both the cross-sell phenomenon and the costs---benefits for the products; (ii) For each version of the B-IMCP problem, we note that the problem turns out to be NP-hard, and then, we present a simple greedy approximation algorithm for the same. We derive the approximation ratio of this greedy algorithm by drawing upon certain key results from the theory of matroids; (iii) We then outline three heuristics based on well-known concepts from the sociology literature; and (iv) Finally, we experimentally compare and contrast the proposed algorithms and heuristics using certain well-known social network data sets such as WikiVote trust network, Epinions, and Telco call detail records data. Based on the experiments, we consistently found that the stronger the cross-sell relationship between the products, the larger the overlap between the seeds of these products and lesser the distances among the corresponding non-overlapping seeds.

Modeling and evaluating information propagation in a microblogging social network

Microblogging platforms, such as Twitter and Plurk, allow users to express feelings, discuss ideas, and share interesting things with their friends or even strangers with similar interests. With the popularity of microblogs, there are growing data and opportunities in understanding information propagation behaviors in online social networks. Though some influence models had been proposed based on certain assumptions, most of them are based on the simulation approach (not data driven). This paper aims at designing a framework to model, measure, evaluate, and visualize influence propagation in a microblogging social network. Considering how information contents are spread in a social network, we devise two influence propagation models from the views of messages posted and responded. Based on the proposed models, we are able to measure the influence capability of an individual with respect to a user-given topic. Our design of influence measures consider (a) the number of people influenced, (b) the speed of propagation, and (c) the geographic distance of the propagation. To test the effectiveness of our influence model, we further propose a novel evaluation framework that predicts the propagation links and influential nodes in a real-world microblogging social network. Finally, we develop an online visualization system allowing users to explore the information propagation with the functions of displaying propagation structures, influence scores of individuals, timelines, and the geographical information for any user-query terms.

Maximizing influence propagation in networks with community structure

We consider the algorithmic problem of selecting a set of target nodes that cause the biggest activation cascade in a network. In case when the activation process obeys the diminishing return property, a simple hill-climbing selection mechanism has been shown to achieve a provably good performance. Here we study models of influence propagation that exhibit critical behavior and where the property of diminishing returns does not hold. We demonstrate that in such systems the structural properties of networks can play a significant role. We focus on networks with two loosely coupled communities and show that the double-critical behavior of activation spreading in such systems has significant implications for the targeting strategies. In particular, we show that simple strategies that work well for homogenous networks can be overly suboptimal and suggest simple modification for improving the performance by taking into account the community structure.