Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 4

Modularity Related Abstracts

4 Comparing Community Detection Algorithms in Bipartite Networks

Authors: Ehsan Khademi, Mahdi Jalili

Abstract:

Despite the special features of bipartite networks, they are common in many systems. Real-world bipartite networks may show community structure, similar to what one can find in one-mode networks. However, the interpretation of the community structure in bipartite networks is different as compared to one-mode networks. In this manuscript, we compare a number of available methods that are frequently used to discover community structure of bipartite networks. These networks are categorized into two broad classes. One class is the methods that, first, transfer the network into a one-mode network, and then apply community detection algorithms. The other class is the algorithms that have been developed specifically for bipartite networks. These algorithms are applied on a model network with prescribed community structure.

Keywords: Complex Networks, Modularity, community detection, bipartite networks, co-clustering, network projection

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3 Product Modularity, Collaboration and the Impact on Innovation Performance in Intra-Organizational R&D Networks

Authors: Daniel Martinez, Tim de Leeuw, Stefan Haefliger

Abstract:

The challenges of managing a large and geographically dispersed R&D organization have been further increasing during the past years, concentrating on the leverage of a geo-graphically dispersed body of knowledge in an efficient and effective manner. In order to reduce complexity and improve performance, firms introduce product modularity as one key element for global R&D network teams to develop their products and projects in collaboration. However, empirical studies on the effects of product modularity on innovation performance are really scant. Furthermore, some researchers have suggested that product modularity promotes innovation performance, while others argue that it inhibits innovation performance. This research fills this gap by investigating the impact of product modularity on various dimensions of innovation performance, i.e. effectiveness and efficiency. By constructing the theoretical framework, this study suggests that that there is an inverted U-shaped relationship between product modularity and innovation performance. Moreover, this research work suggests that the optimum of innovation performance efficiency will be at a higher level than innovation performance effectiveness at a given product modularity level.

Keywords: Networks, Collaboration, R&D, Modularity, innovation performance

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2 A Model Based Metaheuristic for Hybrid Hierarchical Community Structure in Social Networks

Authors: Jalel Akaichi, Radhia Toujani

Abstract:

In recent years, the study of community detection in social networks has received great attention. The hierarchical structure of the network leads to the emergence of the convergence to a locally optimal community structure. In this paper, we aim to avoid this local optimum in the introduced hybrid hierarchical method. To achieve this purpose, we present an objective function where we incorporate the value of structural and semantic similarity based modularity and a metaheuristic namely bees colonies algorithm to optimize our objective function on both hierarchical level divisive and agglomerative. In order to assess the efficiency and the accuracy of the introduced hybrid bee colony model, we perform an extensive experimental evaluation on both synthetic and real networks.

Keywords: Social Network, Modularity, community detection, similarity, metaheuristic, agglomerative hierarchical clustering, divisive hierarchical clustering, bee colony

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1 Community Structure Detection in Networks Based on Bee Colony

Authors: Bilal Saoud

Abstract:

In this paper, we propose a new method to find the community structure in networks. Our method is based on bee colony and the maximization of modularity to find the community structure. We use a bee colony algorithm to find the first community structure that has a good value of modularity. To improve the community structure, that was found, we merge communities until we get a community structure that has a high value of modularity. We provide a general framework for implementing our approach. We tested our method on computer-generated and real-world networks with a comparison to very known community detection methods. The obtained results show the effectiveness of our proposition.

Keywords: Networks, Modularity, bee colony, normalized mutual information

Procedia PDF Downloads 111