Microscopic evolution of social networks

Leskovec, Jure; Bäckström, Lars; Kumar, Raman; Tomkins, Andrew

doi:10.1145/1401890.1401948

Cited by 612 publications

(540 citation statements)

References 26 publications

Supporting

Mentioning

506

Contrasting

Unclassified

Order By: Relevance

“…The first type of approaches is the well-known two- 40 steps strategy, which identifies community structure in each snapshot network by using methods for static networks, and then analyzes the community evolution according to some principles, such as the Jaccard index [13]. The second type of approaches is the generative model [14] which is based on the combination of stochastic block model (SBM) and the state space model or liner dynamic system, i.e., the whole temporal network is represented as a sample of a dynamic generative model.…”

Section: A C C E P T E D Mmentioning

confidence: 99%

Autonomous overlapping community detection in temporal networks: A dynamic Bayesian nonnegative matrix factorization approach

Wang

Jiao

et al. 2016

Knowledge-Based Systems

View full text Add to dashboard Cite

A wide variety of natural or artificial systems can be modeled as time-varying or temporal networks.To understand the structural and functional properties of these time-varying networked systems, it is desirable to detect and analyze the evolving community structure. In temporal networks, the identified communities should reflect the current snapshot network, and at the same time be similar to the communities identified in history or say the previous snapshot networks. Most of the existing approaches assume that the number of communities is known or can be obtained by some heuristic methods. This is unsuitable and complicated for most real world networks, especially temporal networks. In this paper, we propose a Bayesian probabilistic model, named Dynamic Bayesian Nonnegative Matrix Factorization (DBNMF), for automatic detection of overlapping communities in temporal networks. Our model can not only give the overlapping community structure based on the probabilistic memberships of nodes in each snapshot network but also automatically determines the number of communities in each snapshot network based on automatic relevance determination. Thereafter, a gradient descent algorithm is proposed to optimize the objective function of our DBNMF model. The experimental results using both synthetic datasets and real-world temporal networks demonstrate that the DBNMF model has superior performance compared with two widely used methods, especially when the number of communities is unknown and when the network is highly sparse.

show abstract

Section: A C C E P T E D Mmentioning

confidence: 99%

Autonomous overlapping community detection in temporal networks: A dynamic Bayesian nonnegative matrix factorization approach

Wang

Jiao

et al. 2016

Knowledge-Based Systems

View full text Add to dashboard Cite

show abstract

“…users tend to create and receive links in proportion to their outdegree and indegree, respectively. Leskovec et al studied the evolution of Flickr, del.icio.us, Yahoo!Answers, and LinkedIn, and examined whether the new users will preferentially link to the old users with large degrees [24]. They found that Flickr and del.icio.us show linear preference, (k) ∼ k, and Yahoo!Answers shows slightly sublinear preference, (k) ∼ k 0.9 .…”

Section: Preferential Linkingmentioning

confidence: 99%

How people make friends in social networking sites—A microscopic perspective

Wang

2012

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

We study the detailed growth of a social networking site with full temporal information by examining the creation process of each friendship relation that can collectively lead to the macroscopic properties of the network. We first study the reciprocal behavior of users, and find that link requests are quickly responded to and that the distribution of reciprocation intervals decays in an exponential form. The degrees of inviters/accepters are slightly negatively correlative with reciprocation time. In addition, the temporal feature of the online community shows that the distributions of intervals of user behaviors, such as sending or accepting link requests, follow a power law with a universal exponent, and peaks emerge for intervals of an integral day. We finally study the preferential selection and linking phenomena of the social networking site and find that, for the former, a linear preference holds for preferential sending and reception, and for the latter, a linear preference also holds for preferential acceptance, creation, and attachment. Based on the linearly preferential linking, we put forward an analyzable network model which can reproduce the degree distribution of the network. The research framework presented in the paper could provide a potential insight into how the micro-motives of users lead to the global structure of online social networks.

show abstract

“…Do bloggers start linking more with each other or do they tend to stay closer to their initial component? This will tell us about the cohesiveness of these clusters as they form beyond the standard "closing of the triangle" which is a general behavior of social networks (see, e.g., Wasserman and Faust 1994;Leskovec et al 2008a;Lazer et al 2010), but perhaps less so in the blogosphere when we are analyzing topics. In this case it may make more sense to talk about components or groups and monitor how far communication goes outside the initial groups as they start merging into larger communities.…”

Section: Temporal Analysis Of Communitiesmentioning

confidence: 99%

Contextual linking behavior of bloggers: leveraging text mining to enable topic-based analysis

Macskassy

2011

Soc. Netw. Anal. Min.

View full text Add to dashboard Cite

The last decade has seen an explosion in blogging and the blogosphere is continuing to grow, having a large global reach and many vibrant communities. Researchers have been pouring over blog data with the goal of finding communities, tracking what people are saying, finding influencers, and using many social network analytic tools to analyze the underlying social networks embedded within the blogosphere. One of the key technical problems with analyzing large social networks such as those embedded in the blogosphere is that there are many links between individuals and we often do not know the context or meaning of those links. This is problematic because it makes it difficult if not impossible to tease out the true communities, their behavior, how information flows, and who the central players are (if any). This paper seeks to further our understanding of how to analyze large blog networks and what they can tell us. We analyze 1.13M blogs posted by 185K bloggers over a period of 3 weeks. These bloggers span private blog sites through large blog-sites such as LiveJournal and Blogger. We show that we can, in fact, tag links in meaningful ways by leveraging topic-detection over the blogs themselves. We use these topics to contextually tag links coming from a particular blog post. This enrichment enables us to create smaller topic-specific graphs which we can analyze in some depth. We show that these topic-specific graphs not only have a different topology from the general blog graph but also enable us to find central bloggers which were otherwise hard to find. We further show that a temporal analSofus A. Macskassy Fetch Technologies 841 Apollo Street, Suite 400 El Segundo, CA 90245, USA Tel: +310-414-9849 E-mail: sofmac@fetch.com ysis identifies behaviors in terms of how components form as well as how bloggers continue to link after components form. These behaviors come to light when doing an analysis on the topic-specific graphs but are hidden or not easily discernable when analyzing the general blog graph.

show abstract

Microscopic evolution of social networks

Cited by 612 publications

References 26 publications

Autonomous overlapping community detection in temporal networks: A dynamic Bayesian nonnegative matrix factorization approach

Autonomous overlapping community detection in temporal networks: A dynamic Bayesian nonnegative matrix factorization approach

How people make friends in social networking sites—A microscopic perspective

Contextual linking behavior of bloggers: leveraging text mining to enable topic-based analysis

Contact Info

Product

Resources

About