Detecting topics and overlapping communities in Question and Answer sites

International audienceIn many social networks, people interact based on their interests. Community detection algorithms are then useful to reveal the sub-structures of a network and in particular interest groups. Identifying these users' communities and the interests that bind them can help us assist their life-cycle. Certain kinds of online communities such as question-and-answer (Q&A) sites, have no explicit social network structure. Therefore, many traditional community detection techniques do not apply directly. In this paper, we propose an efficient approach for extracting topic from Q&A to detect communities of interest. Then we compare three detection methods we applied on a dataset extracted from the popular Q&A site StackOverflow. Our method based on topic modeling and user membership assignment is shown to be much simpler and faster while preserving the quality of the detection

Community Detection in Complex Networks

Finding communities of connected individuals in social networks is essential for understanding our society and interactions within the network. Recently attention has turned to analyse these communities in complex network systems. In this thesis, we study three challenges. Firstly, analysing and evaluating the robustness of new and existing score functions as these functions are used to assess the community structure for a given network. Secondly, unfolding community structures in static social networks. Finally, detecting the dynamics of communities that change over time. The score functions are evaluated on different community structures. The behaviour of these functions is studied by migrating nodes randomly from their community to a random community in a given true partition until all nodes will be migrated far from their communities. Then Multi-Objective Evolutionary Algorithm Based Community Detection in Social Networks (MOEA-CD) is used to capture the intuition of community identi cation with dense connections within the community and sparse with others. This algorithm redirects the design of objective functions according to the nodes' relations within community and with other communities. This new model includes two new contradictory objectives, the rst is to maximise the internal neighbours for each node within a community and the second is to minimise the maximum external links for each node within a community with respect to its internal neighbours. Both of these objectives are optimised simultaneously to nd a set of estimated Pareto-optimal solutions where each solution corresponds to a network partition. Moreover, we propose a new local heuristic search, namely, the Neighbour Node Centrality (NNC) strategy which is combined with the proposed model to improve the performance of MOEA-CD to nd a local optimal solution. We also design an algorithm which produces community structures that evolve over time. Recognising that there may be many possible community structures that ex- plain the observed social network at each time step, in contrast to existing methods, which generally treat this as a coupled optimisation problem, we formulate the prob- lem in a Hidden Markov Model framework, which allows the most likely sequence of communities to be found using the Viterbi algorithm where there are many candi- date community structures which are generated using Multi-Objective Evolutionary Algorithm. To demonstrate that our study is effective, it is evaluated on synthetic and real-life dynamic networks and it is used to discover the changing Twitter communities of MPs preceding the Brexit referendum

Community Detection in Complex Networks

Finding communities of connected individuals in social networks is essential for understanding our society and interactions within the network. Recently attention has turned to analyse these communities in complex network systems. In this thesis, we study three challenges. Firstly, analysing and evaluating the robustness of new and existing score functions as these functions are used to assess the community structure for a given network. Secondly, unfolding community structures in static social networks. Finally, detecting the dynamics of communities that change over time. The score functions are evaluated on different community structures. The behaviour of these functions is studied by migrating nodes randomly from their community to a random community in a given true partition until all nodes will be migrated far from their communities. Then Multi-Objective Evolutionary Algorithm Based Community Detection in Social Networks (MOEA-CD) is used to capture the intuition of community identi cation with dense connections within the community and sparse with others. This algorithm redirects the design of objective functions according to the nodes' relations within community and with other communities. This new model includes two new contradictory objectives, the rst is to maximise the internal neighbours for each node within a community and the second is to minimise the maximum external links for each node within a community with respect to its internal neighbours. Both of these objectives are optimised simultaneously to nd a set of estimated Pareto-optimal solutions where each solution corresponds to a network partition. Moreover, we propose a new local heuristic search, namely, the Neighbour Node Centrality (NNC) strategy which is combined with the proposed model to improve the performance of MOEA-CD to nd a local optimal solution. We also design an algorithm which produces community structures that evolve over time. Recognising that there may be many possible community structures that ex- plain the observed social network at each time step, in contrast to existing methods, which generally treat this as a coupled optimisation problem, we formulate the prob- lem in a Hidden Markov Model framework, which allows the most likely sequence of communities to be found using the Viterbi algorithm where there are many candi- date community structures which are generated using Multi-Objective Evolutionary Algorithm. To demonstrate that our study is effective, it is evaluated on synthetic and real-life dynamic networks and it is used to discover the changing Twitter communities of MPs preceding the Brexit referendum

基調構造を利用したグラフクラスタリングの高速化

モジュラリティクラスタリングは複雑な構造を持ったグラフから密な接続を持つクラスタを特定する手法であり，グラフ分析応用において重要な要素技術となっている．しかしながら，大規模なグラフを対象とした場合，モジュラリティクラスタリングには(1) クラスタの精度が著しく低下する，(2) クラスタリングに膨大な計算時間を要するという問題がある．これらの問題に対しこれまで様々な手法が提案されてきたが，依然として両問題を同時に解決する手法は存在しない．そこで本稿では，大規模なグラフに対して高速かつ高精度にクラスタを検出する手法gScarf法を提案する．gScarf 法では，既存の高精度なモジュラリティクラスタリング手法を基に，グラフの基調構造（トポロジー）を利用した高速化を行う．これにより，gScarf 法は高いクラスタリング精度を保ちつつ，高速なクラスタリングを大規模なグラフに対して実現する．本稿では実データならびに人工データを用いた評価実験を行い，近年提案された手法と比較してgScarf 法は高精度なクラスタを最大1,100 倍程度高速に計算できることを確認した．第12回データ工学と情報マネジメントに関するフォーラム (DEIM2020)日時：2020年3月2日～4日　オンライン開