Social influence analysis in large-scale networks

Tang, Jie; Sun, Jimeng; Wang, Chi; Yang, Zi

doi:10.1145/1557019.1557108

Cited by 788 publications

(501 citation statements)

References 19 publications

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“…Several studies design machine learning algorithms to generate reasonable influence graphs by studying practical influence cascade model parameters from real datasets [17], [18], [19], [20].…”

Section: Related Workmentioning

confidence: 99%

Influential Neighbours Selection for Information Diffusion in Online Social Networks

Kim

Yoneki

2012

2012 21st International Conference on Computer Communications and Networks (ICCCN)

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Abstract-The problem of maximizing information diffusion through a network is a topic of considerable recent interest. A conventional problem is to select a set of any arbitrary k nodes as the initial influenced nodes so that they can effectively disseminate the information to the rest of the network. However, this model is usually unrealistic in online social networks since we cannot typically choose arbitrary nodes in the network as the initial influenced nodes. From the point of view of an individual user who wants to spread information as much as possible, a more reasonable model is to try to initially share the information with only some of its neighbours rather than a set of any arbitrary nodes; but how can these neighbours be effectively chosen?We empirically study how to design more effective neighbours selection strategies to maximize information diffusion. Our experimental results through intensive simulation on several realworld network topologies show that an effective neighbours selection strategy is to use node degree information for shortterm propagation while a naive random selection is also adequate for long-term propagation to cover more than half of a network. We also discuss the effects of the number of initial activated neighbours. If we particularly select the highest degree nodes as initial activated neighbours, the number of initial activated neighbours is not an important factor at least for long-term propagation of information.

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Section: Related Workmentioning

confidence: 99%

Influential Neighbours Selection for Information Diffusion in Online Social Networks

Kim

Yoneki

2012

2012 21st International Conference on Computer Communications and Networks (ICCCN)

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“…We perform our experiments on three data sets, which were adopted in [23]. As summarized in Table 1, two data sets, co-author network (shortly AN) and paper citation network (shortly CN), are extracted from academia search system Arnetminer, and the last one, movie network (shortly MN), is crawled from Wikipedia category "English-language films".…”

Section: Methodsmentioning

confidence: 99%

Privacy Preserving Group Linkage

Chen

Luo

et al. 2011

Lecture Notes in Computer Science

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Abstract. The problem of privacy-preserving record linkage is to find the intersection of records from two parties, while not revealing any private records to each other. Recently, group linkage has been introduced to measure the similarity of groups of records [19]. When we extend the traditional privacy-preserving record linkage methods to group linkage measurement, group membership privacy becomes vulnerable -record identity could be discovered from unlinked groups. In this paper, we introduce threshold privacy-preserving group linkage (TPPGL) schemes, in which both parties only learn whether or not the groups are linked. Therefore, our approach is secure under group membership inference attacks. In experiments, we show that using the proposed TPPGL schemes, group membership privacy is well protected against inference attacks with a reasonable overhead.

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“…These considerations highlight the need of, (1) methods that can take benefit of additional information associated to the nodes (the users) of a social network (e.g., demographics, behavioral information), and (2), methods to incorporate topic modeling in the influence analysis. While some preliminary work in this direction exists [58], [18], [59], we believe that the synergy of topic modeling and influence analysis is still in its infancy, and we expect this to become an hot research area in the next years.…”

Section: Concluding Remarks and Open Problemsmentioning

confidence: 99%

Influence Propagation in Social Networks: A Data Mining Perspective

Bonchi

2011

2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology

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Abstract-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.

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Social influence analysis in large-scale networks

Cited by 788 publications

References 19 publications

Influential Neighbours Selection for Information Diffusion in Online Social Networks

Influential Neighbours Selection for Information Diffusion in Online Social Networks

Privacy Preserving Group Linkage

Influence Propagation in Social Networks: A Data Mining Perspective

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