Community structure of complex software systems: Analysis and applications

Due to notable discoveries in the fast evolving field of complex networks, recent research in software engineering has also focused on representing software systems with networks. Previous work has observed that these networks follow scale-free degree distributions and reveal small-world phenomena, while we here explore another property commonly found in different complex networks, i.e. community structure. We adopt class dependency networks, where nodes represent software classes and edges represent dependencies among them, and show that these networks reveal a significant community structure, characterized by similar properties as observed in other complex networks. However, although intuitive and anticipated by different phenomena, identified communities do not exactly correspond to software packages. We empirically confirm our observations on several networks constructed from Java and various third party libraries, and propose different applications of community detection to software engineering

Link Prediction in Complex Networks: A Survey

Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.Comment: 44 pages, 5 figure

Predicting Missing Links via Local Information

Missing link prediction of networks is of both theoretical interest and practical significance in modern science. In this paper, we empirically investigate a simple framework of link prediction on the basis of node similarity. We compare nine well-known local similarity measures on six real networks. The results indicate that the simplest measure, namely common neighbors, has the best overall performance, and the Adamic-Adar index performs the second best. A new similarity measure, motivated by the resource allocation process taking place on networks, is proposed and shown to have higher prediction accuracy than common neighbors. It is found that many links are assigned same scores if only the information of the nearest neighbors is used. We therefore design another new measure exploited information of the next nearest neighbors, which can remarkably enhance the prediction accuracy.Comment: For International Workshop: "The Physics Approach To Risk: Agent-Based Models and Networks", http://intern.sg.ethz.ch/cost-p10

Routing-Aware Time Slot Allocation Heuristics in Contention-Free Sensor Networks

Part 7: Wireless Sensor NetworksInternational audienceTraditionally, in Wireless Sensor Networks, protocols are designed independently in the layered protocol stack, and metrics involved in several layers can be affected. Communication latency is one metric example, impacted by both the routing protocol in the network layer and the MAC protocol in the data link layer. Better performances can be obtained using cross-layer approaches.In this paper, we address latency optimizations for communications in sensor networks, based on cross-layer decisions. More particularly, we propose new time slot scheduling methods correlated to routing decisions. Slot allocation for nodes follows particular routing tree traversals, trying to reduce the gap between the slot of a child and that of its parent.Simulations show that latency performance of our contributions improves similar cross-layer approaches from 33 % up to 54 %. Duty cycle of obtained schedules are also improved from 7 % up to 11 %