Abstract not found

Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics and function of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of measurements capable of expressing the most relevant topological features. This article presents a survey of such measurements. It includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements organized into classes. Special attention is given to relating complex network analysis with the areas of pattern recognition and feature selection, as well as on surveying some concepts and measurements from traditional graph theory which are potentially useful for complex network research. Depending on the network and the analysis task one has in mind, a specific set of features may be chosen. It is hoped that the present survey will help the

Commercial software project managers design project organizational structure carefully, mindful of available skills, division of labour, geographical boundaries, etc. These organizational “cathedrals ” are to be contrasted with the “bazaarlike” nature of Open Source Software (OSS) Projects, which have no pre-designed organizational structure. Any structure that exists is dynamic, self-organizing, latent, and usually not explicitly stated. However, in large, complex, successful, OSS projects, we expect that sub-communities will form organically within the “bazaar ” of developer teams. Studying these sub-communities, and their behavior can shed light on how successful OSS projects self-organize. This phenomenon could even hold important lessons for how commercial software teams might be organized. Building on wellestablished techniques for detecting community structure in complex networks, we extract and evaluate latent subcommunities from the email social network of several projects: Apache HTTPD, Python, PostgresSQL, Perl, and Apache ANT. We then validate them with software development activity history. Our results show that subcommunities do indeed form within these projects. We find, in other words, that “chapels ” (if not cathedrals) spontaneously arise within the bazaar as OSS systems and the teams evolve. We also find that these subgroups manifest most strongly in technical discussions, and are significantly connected with collaboration behaviour. 1.

In many networks, it is of great interest to identify communities, unusually densely knit groups of individuals. Such communities often shed light on the function of the networks or underlying properties of the individuals. Recently, Newman suggested modularity as a natural measure of the quality of a network partitioning into communities. Since then, various algorithms have been proposed for (approximately) maximizing the modularity of the partitioning determined. In this paper, we introduce the technique of rounding mathematical programs to the problem of modularity maximization, presenting two novel algorithms. More specifically, the algorithms round solutions to linear and vector programs. Importantly, the linear programing algorithm comes with an a posteriori approximation guarantee: by comparing the solution quality to the fractional solution of the linear program, a bound on the available “room for improvement ” can be obtained. The vector programming algorithm provides a similar bound for the best partition into two communities. We evaluate both algorithms using experiments on several standard test cases for network partitioning algorithms, and find that they perform comparably or better than past algorithms, while being more efficient than exhaustive techniques.

been adapted to modularity clustering. Section 4 details the single-level and multi-level refinement heuristics, and Section 5.3 compares them experimentally. Because the effectiveness of (particularly multi-level) refinement may depend on the coarsening algorithm, Section 5.4 examines various combinations of coarsening and refinement heuristics. Section 6 compares public implementations and benchmark results of modularity clustering heuristics, without a restriction to coarsening and refinement algorithms. While this is one of the most extensive comparisons in the literature, it is far from exhaustive, because implementations and sufficient experimental results have not been published for some proposed heurisarXiv:0812.4073v1

Automatic discovery of community structures in complex networks is a fundamental task in many disciplines, including social science, engineering, and biology. Recently, a quantitative measure called modularity (Q) has been proposed to effectively assess the quality of community structures. Several community discovery algorithms have since been developed based on the optimization of Q. However, this optimization problem is NP-hard, and the existing algorithms have a low accuracy or are computationally expensive. In this paper, we present an efficient spectral algorithm for modularity optimization. When tested on a large number of synthetic or real-world networks, and compared to the existing algorithms, our method is efficient and and has a high accuracy. We demonstrate our algorithm on three applications in biology, medicine, and social science. In the first application, we analyze the communities in a gene network, and show that genes in the same community usually have very similar functions, which enables us to predict functions for some new genes. Second, we apply the algorithm to group tumor samples based on gene expression microarray data. Remarkably, our algorithm can automatically detect different types of tumor without any prior knowledge, and by combining our results and clinical information, we can predict the outcomes of chemotherapies with a high accuracy. Finally, we analyze a social network of Usenet newsgroup users, and show that, without any semantic information, we can discover the organization of the newsgroups, and detect users groups with similar interests. 1

The dynamic social network visualizer “DySoN ” (Dynamic Social Networks) aims at understanding patterns and structural changes in dynamic social networks that evolve over time via an interactive visualization approach. As an alternative and supplementation to the numerous other approaches to visualization of social network data and as an attempt to overcome some of the drawbacks of these approaches, DySoN interactively visualizes streaming event data of social interactions by an interactive three-dimensional model of interpolated NURBS ”tubes”, representing activity and social proximity within a given set of actors during a given time period by using three dimensions of temporal information mapping: spatial density (tube distance), tubecolor and tube-diameter. We use a self assembled large collaboration network of Jazz musicians with a straightforward semantics for the computation of relation strengths for the evaluation of the approach. We also discuss applications of the concept for awareness services in mobile peer to peer social networks, which exhibit a vivid measurable social micro dynamics in time and space.

Identifying overlapping communities in networks is a challenging task. In this work we present a novel approach to community detection that utilises the Bayesian non-negative matrix factorisation (NMF) model to produce a probabilistic output for node memberships. The scheme has the advantage of computational efficiency, soft community membership and an intuitive foundation. We present the performance of the method against a variety of benchmark problems and compare and contrast it to several other algorithms for community detection. Our approach performs favourably compared to other methods at a fraction of the computational costs.

We have sampled the artist social network of Myspace and to it applied the pairwise relational connectivity measure Minimum cut/Maximum flow. These values are then compared to a pairwise acoustic Earth Mover’s Distance measure and the relationship is discussed. Further, a means of constructing playlists using the maximum flow value to exploit both the social and acoustic distances is realized. 1

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