Nan Du

Community detection in large-scale social networks

Recent years have seen that WWW is becoming a flourishing social media which enables individuals to easily share opinions, experiences and expertise at the push of a single button. With the pervasive usage of instant messaging systems and the fundamental shift in the ease of publishing content, social network researchers and graph theory researchers are now concerned with inferring community structures by analyzing the linkage patterns among individuals and web pages. Although the investigation of community structures has motivated many diverse algorithms, most of them are unsuitable for large-scale social networks because of the computational cost. Moreover, in addition to identify the possible community structures, how to define and explain the discovered communities is also significant in many practical scenarios.n In this paper, we present the algorithm ComTector(Community DeTector) which is more efficient for the community detection in large-scale social networks based on the nature of overlapping communities in the real world. This algorithm does not require any priori knowledge about the number or the original division of the communities. Because real networks are often large sparse graphs, its running time is thus O(C × Tri2), where C is the number of the detected communities and Tri is the number of the triangles in the given network for the worst case. Then we propose a general naming method by combining the topological information with the entity attributes to define the discovered communities. With respected to practical applications, ComTector is challenged with several real life networks including the Zachary Karate Club, American College Football, Scientific Collaboration, and Telecommunications Call networks. Experimental results show that this algorithm can extract meaningful communities that are agreed with both of the objective facts and our intuitions.

A Parallel Algorithm for Enumerating All Maximal Cliques in Complex Network

Efficient enumeration of all maximal cliques in a given graph has many applications in graph theory, data mining and bio informatics. However, the exponentially increasing computation time of this problem confines the scale of the graph. Meanwhile, recent researches show that many networks in our world are complex networks involving massive data. To solve the maximal clique problem in the real-world scenarios, this paper presents a parallel algorithm Peamc (parallel enumeration of all maximal cliques) which exploits several new and effective techniques to enumerate all maximal cliques in a complex network. Furthermore, we provide a performance study on a true-life call graph with up to 2,423,807 vertices and 5,317,183 edges. The experimental results show that Peamc can find all the maximal cliques in a complex network with high efficiency and scalability

Overlapping Community Detection in Bipartite Networks

Researches have discovered that rich interactions among entities in nature and human society bring about complex networks with community structures. In this paper, we propose a novel algorithm BiTector (bi-community detector) to mine the overlapping communities in large-scale sparse bipartite networks. We apply the algorithm to various real-world datasets, showing that BiTector can identify the overlapping community structures in the bipartite networks efficiently and effectively.

Community detection in complex networks

With the rapidly growing evidence that various systems in nature and society can be modeled as complex networks, community detection in networks becomes a hot research topic in physics, sociology, computer society, etc. Although this investigation of community structures has motivated many diverse algorithms, most of them are unsuitable when dealing with large networks due to their computational cost. In this paper, we present a faster algorithm ComTector, which is more efficient for the community detection in large complex networks based on the nature of overlapping cliques. This algorithm does not require any priori knowledge about the number or the original division of the communities. With respect to practical applications, ComTector is challenging with five different types of networks including the classic Zachary Karate Club, Scientific Collaboration Network, South Florida FreeWord Association Network, Urban Traffic Network, North America Power Grid and the Telecommunication Call Network. Experimental results show that our algorithm can discover meaningful communities that meet both the objective basis and our intuitions.

Cell phone mini challenge award: Social network accuracy— exploring temporal communication in mobile call graphs

In the mobile call mini challenge of VAST 2008 contest, we explored the temporal communication patterns of Catalano/Vidro social network which is reflected in the mobile call data. We focus on detecting the hierarchy of the social network and try to get the important actors in it. We present our tools and methods in this summary. By using the visual analytic approaches, we can find out not only the temporal communication patterns in the social network but also the hierarchy of it.

Overlapping community structure detection in networks

Many systems in nature and human society take the form of networks with community structures. In this paper, we describe a simple algorithm COCD(Clique-based Overlapping Community Detection) to efficiently mine the overlapping communities in large-scale networks, which is useful for us to have a better understanding of the nested sub-structures embedded in the whole network.

A new algorithm for enumerating all maximal cliques in complex network

In this paper, we consider the problem of enumerating all maximal cliques in a complex network G = (V, E) with n vertices and m edges. We propose an algorithm for enumerating all maximal cliques based on researches of the complex network properties. A novel branch and bound strategy by considering the clustering coefficient of a vertex is proposed. Our algorithm runs with time O (d^2*N*S) delay and in O (n + m) space. It requires O (n*D^2) time as a preprocessing, where D, N, S, d denote the maximum degree of G, the number of maximal cliques, the size of the maximum clique, and the number of triangles of a vertex with degree D respectively. Finally, we apply our algorithm to the telecommunication customer-churn-prediction and the experimental results show that the application promotes the capabilities of the churn prediction system effectively.

Backbone Discovery in Social Networks

Recent years have seen a thriving development of the World Wide Web as the most visible social media which enables people to share opinions, experiences and expertise with each other across the world. People now get involved in many different social networks simultaneously, which are often large intricate web of connections among the massive entities they are made of. As a result, the challenge of collecting and analyzing large-scale data among social members has left most basic questions about the global composition and function of such networks largely unresolved: What is the essential organization of a social network? who are the influential individuals whose voice is echoed by others? To address these questions, this paper presents an algorithm called sketcher to discover and describe the overall backbone of a specific network. Experimental results on the American College Football, Scientific Collaboration, and Telecommunications Call networks show that sketcher can extract the essential composition of a social network both efficiently and intuitively.

Qos-based Algorithm for Job Allocation and Scheduling in Data Grid

Job allocation and scheduling for data transfer is a fundamental issue for achieving high performance in data grid environments. In this paper, we propose a new algorithm that combines job allocation with scheduling dynamically based on resource quality. The algorithm takes resource failure into consideration and provides a re-allocation mechanism, so it can utilize limited amounts of resources efficiently and enhance the reliability of data transfer in data grid. A definition of resource quality is given in the paper as well, which consists of information about CPU and bandwidth of the grid storage node that resource resides. To reflect historical performance of resource, a new instance of ant algorithm is designed for calculating and updating this resource quality. Based on this quality, the job allocation and scheduling algorithm can take full advantage of the high performance resources and balance the load among resources at the same time. Experimental results show that the algorithm satisfies the expectations

Community Ranking in Social Network

Social network is one of the most important true-life networks in our real world scenarios. A typical feature of the social network is the dense sub-structure (quasi-clique or community) which is essential for understanding the networks internal structure and function. Traditional social network analysis usually focuses on the centrality and power of a single individual or entity, however, in peoples daily life, a group or an organization often holds a more influential position and plays a more important role. Therefore, in this paper, we first present a parallel algorithm for the detection of quasi-cliques, and then we describe the techniques that are useful for evaluating the centrality and significance of a quasi-clique. Computational results on a real call graph from a telecom career and a collaboration network of co-authors are given in the end.