The fast growth of social networks and their privacy requirements in recent years, has lead to increasing difficulty in obtaining complete topology of these networks. However, diffusion information over these networks is available and many algorithms have been proposed to infer the underlying networks by using this information. The previously proposed algorithms only focus on inferring more links and do not pay attention to the important characteristics of the underlying social networks In this paper, we propose a novel algorithm, called DANI, to infer the underlying network structure while preserving its properties by using the diffusion information. Moreover, the running time of the proposed method is considerably lower than the previous methods. We applied the proposed method to both real and synthetic networks. The experimental results showed that DANI has higher accuracy and lower run time compared to well-known network inference methods.
Discovering underlying communities in networks is an important task in network analysis. In the last decade, a large variety of algorithms have been proposed. However, most of them require global information or a centralized control. Those algorithms are infeasible in large-scale real networks due to computation and accessibility. In this paper, we propose a novel decentralized community detection algorithm based on information diffusion. We believe information diffusion in human society can allow us to understand the emergence of community structure. Being able to find out some critical nodes which play an important role in the formation of a community is an important byproduct for our algorithm. Experiments on various networks, including benchmark networks and synthetic networks, show that it is comparable to three decentralized algorithms and two representative centralized algorithms, in terms of stability and accuracy. Community discovery is one of the most important steps to understand the social networks. We propose a hierarchical diffusion method to detect the community structure. Our algorithm is based on the idea that people in different communities usually share less common friends. We also make use of the fact that people usually make decisions based others’choices, especially their friends’. Our algorithm can distinguish between pseudo-communities and meaningful ones. Tests on both classical and synthetic benchmarks show that our algorithm is comparable to state-of-the-art community detection algorithms in both computational complexity and accuracy measured by the so-called normalized mutual information. The recent boom of large-scale online social networks (OSNs) both enables and necessitates the use of parallelizable and scalable computational techniques for their analysis. We examine the problem of real-time community detection and a recently proposed linear time--- @math on a network with @math edges---label propagation, or epidemic'' community detection algorithm. We identify characteristics and drawbacks of the algorithm and extend it by incorporating different heuristics to facilitate reliable and multifunctional real-time community detection. With limited computational resources, we employ the algorithm on OSN data with @math nodes and about @math directed edges. Experiments and benchmarks reveal that the extended algorithm is not only faster but its community detection accuracy compares favorably over popular modularity-gain optimization algorithms known to suffer from their resolution limits. Research has shown that many social networks come into being hierarchically based on some basic building blocks called communities, within which the social interactions are very intensive, but between which they are very weak. Network community mining algorithms aim at efficiently and effectively discovering all such communities from a given network. Many related methods have been proposed and applied to different areas including social network analysis, gene network analysis and web clustering engine. Most of the existing methods for mining communities are centralized. In this paper, we present a multi-agent based decentralized algorithm, in which a group of autonomous agents work together to mine a network through a proposed self-aggregation and self-organization mechanism. Thanks to its decentralized feature, our method is potentially suitable for dealing with distributed networks, whose global structures are hard to obtain due to their geographical distributions, decentralized controls or huge sizes. The effectiveness of our method has been tested against different benchmark networks.  Community detection and analysis is an important methodology for understanding the organization of various real-world networks and has applications in problems as diverse as consensus formation in social communities or the identification of functional modules in biochemical networks. Currently used algorithms that identify the community structures in large-scale real-world networks require a priori information such as the number and sizes of communities or are computationally expensive. In this paper we investigate a simple label propagation algorithm that uses the network structure alone as its guide and requires neither optimization of a predefined objective function nor prior information about the communities. In our algorithm every node is initialized with a unique label and at every step each node adopts the label that most of its neighbors currently have. In this iterative process densely connected groups of nodes form a consensus on a unique label to form communities. We validate the algorithm by applying it to networks whose community structures are known. We also demonstrate that the algorithm takes an almost linear time and hence it is computationally less expensive than what was possible so far.