Community Detection via Semi-Synchronous Label Propagation Algorithms

A recently introduced novel community detection strategy is based on a label propagation algorithm (LPA) which uses the diffusion of information in the network to identify communities. Studies of LPAs showed that the strategy is effective in finding a good community structure. Label propagation step can be performed in parallel on all nodes (synchronous model) or sequentially (asynchronous model); both models present some drawback, e.g., algorithm termination is nor granted in the first case, performances can be worst in the second case. In this paper, we present a semi-synchronous version of LPA which aims to combine the advantages of both synchronous and asynchronous models. We prove that our models always converge to a stable labeling. Moreover, we experimentally investigate the effectiveness of the proposed strategy comparing its performance with the asynchronous model both in terms of quality, efficiency and stability. Tests show that the proposed protocol does not harm the quality of the partitioning. Moreover it is quite efficient; each propagation step is extremely parallelizable and it is more stable than the asynchronous model, thanks to the fact that only a small amount of randomization is used by our proposal.Comment: In Proc. of The International Workshop on Business Applications of Social Network Analysis (BASNA '10

MAP: Microblogging Assisted Profiling of TV Shows

Online microblogging services that have been increasingly used by people to share and exchange information, have emerged as a promising way to profiling multimedia contents, in a sense to provide users a socialized abstraction and understanding of these contents. In this paper, we propose a microblogging profiling framework, to provide a social demonstration of TV shows. Challenges for this study lie in two folds: First, TV shows are generally offline, i.e., most of them are not originally from the Internet, and we need to create a connection between these TV shows with online microblogging services; Second, contents in a microblogging service are extremely noisy for video profiling, and we need to strategically retrieve the most related information for the TV show profiling.To address these challenges, we propose a MAP, a microblogging-assisted profiling framework, with contributions as follows: i) We propose a joint user and content retrieval scheme, which uses information about both actors and topics of a TV show to retrieve related microblogs; ii) We propose a social-aware profiling strategy, which profiles a video according to not only its content, but also the social relationship of its microblogging users and its propagation in the social network; iii) We present some interesting analysis, based on our framework to profile real-world TV shows

Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation

Many security and privacy problems can be modeled as a graph classification problem, where nodes in the graph are classified by collective classification simultaneously. State-of-the-art collective classification methods for such graph-based security and privacy analytics follow the following paradigm: assign weights to edges of the graph, iteratively propagate reputation scores of nodes among the weighted graph, and use the final reputation scores to classify nodes in the graph. The key challenge is to assign edge weights such that an edge has a large weight if the two corresponding nodes have the same label, and a small weight otherwise. Although collective classification has been studied and applied for security and privacy problems for more than a decade, how to address this challenge is still an open question. In this work, we propose a novel collective classification framework to address this long-standing challenge. We first formulate learning edge weights as an optimization problem, which quantifies the goals about the final reputation scores that we aim to achieve. However, it is computationally hard to solve the optimization problem because the final reputation scores depend on the edge weights in a very complex way. To address the computational challenge, we propose to jointly learn the edge weights and propagate the reputation scores, which is essentially an approximate solution to the optimization problem. We compare our framework with state-of-the-art methods for graph-based security and privacy analytics using four large-scale real-world datasets from various application scenarios such as Sybil detection in social networks, fake review detection in Yelp, and attribute inference attacks. Our results demonstrate that our framework achieves higher accuracies than state-of-the-art methods with an acceptable computational overhead.Comment: Network and Distributed System Security Symposium (NDSS), 2019. Dataset link: http://gonglab.pratt.duke.edu/code-dat

The Community Structure of R&D Cooperation in Europe. Evidence from a social network perspective

The focus of this paper is on pre-competitive R&D cooperation across Europe, as captured by R&D joint ventures funded by the European Commission in the time period 1998-2002, within the 5th Framework Program. The cooperations in this Framework Program give rise to a bipartite network with 72,745 network edges between 25,839 actors (representing organizations that include firms, universities, research organizations and public agencies) and 9,490 R&D projects. With this construction, participating actors are linked only through joint projects. In this paper we describe the community identification problem based on the concept of modularity, and use the recently introduced label-propagation algorithm to identify communities in the network, and differentiate the identified communities by developing community-specific profiles using social network analysis and geographic visualization techniques. We expect the results to enrich our picture of the European Research Area by providing new insights into the global and local structures of R&D cooperation across Europe

Distributed Community Detection in Dynamic Graphs

Inspired by the increasing interest in self-organizing social opportunistic networks, we investigate the problem of distributed detection of unknown communities in dynamic random graphs. As a formal framework, we consider the dynamic version of the well-studied \emph{Planted Bisection Model} \sdG(n,p,q) where the node set $[n]$ of the network is partitioned into two unknown communities and, at every time step, each possible edge $(u,v)$ is active with probability $p$ if both nodes belong to the same community, while it is active with probability $q$ (with $q<<p$) otherwise. We also consider a time-Markovian generalization of this model. We propose a distributed protocol based on the popular \emph{Label Propagation Algorithm} and prove that, when the ratio $p/q$ is larger than $n^{b}$ (for an arbitrarily small constant $b>0$), the protocol finds the right "planted" partition in $O(\log n)$ time even when the snapshots of the dynamic graph are sparse and disconnected (i.e. in the case $p=\Theta(1/n)$).Comment: Version I