Yuanjun Bi

Least Cost Rumor Blocking in Social Networks

In many real-world scenarios, social network serves as a platform for information diffusion, alongside with positive information (truth) dissemination, negative information (rumor) also spread among the public. To make the social network as a reliable medium, it is necessary to have strategies to control rumor diffusion. In this article, we address the Least Cost Rumor Blocking (LCRB) problem where rumors originate from a community Cr in the network and a notion of protectors are used to limit the bad influence of rumors. The problem can be summarized as identifying a minimal subset of individuals as initial protectors to minimize the number of people infected in neighbor communities of Cr at the end of both diffusion processes. Observing the community structure property, we pay attention to a kind of vertex set, called bridge end set, in which each node has at least one direct in-neighbor in Cr and is reachable from rumors. Under the OOAO model, we study LCRB-P problem, in which α (0 <; α <; 1) fraction of bridge ends are required to be protected. We prove that the objective function of this problem is submodular and a greedy algorithm is adopted to derive a (1-1/e)-approximation. Furthermore, we study LCRB-D problem over the DOAA model, in which all the bridge ends are required to be protected, we prove that there is no polynomial time o(ln n)-approximation for the LCRB-D problem unless P = NP, and propose a Set Cover Based Greedy (SCBG) algorithm which achieves a O(ln n)-approximation ratio. Finally, to evaluate the efficiency and effectiveness of our algorithm, we conduct extensive comparison simulations in three real-world datasets, and the results show that our algorithm outperforms other heuristics.

Better approximation algorithms for influence maximization in online social networks

Influence maximization is a classic and hot topic in social networks. In this paper, firstly we argue that in online social networks, due to the time sensitivity of popular topics, the assumption in IC or LT model that the influence propagates endlessly in the network, is not applicable. Based on this we consider influence transitivity and limited propagation distance in our new model. Secondly, under our model we propose Semidefinite based algorithms. While most existing algorithms rely on monotony and submodularity to obtain approximation ratio of

Influence and Profit: Two Sides of the Coin

1-1/e

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

1−1/e, when no size limitation exists on the number of seeds, our algorithm achieves approximation ratio with

The Maximum Community Partition Problem in Networks

0.857

Community Expansion in Social Network

0.857, which is a great improvement. Moreover, when only a limited number of nodes can be chosen as seeds, based on computer-assisted proof, we claim our algorithm still keeps approximation ratio higher than