Using content and interactions for discovering communities in social networks

2016

Neural Comput & Applic

Alonso-Nanclares, JA.; Paredes Palacios, R.; Rosso, P. (2016). Feature representation for social circles detection using MAC. Neural Computing and Applications. 1-8. doi:10.1007/s00521-016-2222-y.Noname manuscript No. Abstract Social circles detection is a special case of community detection in social network that is currently attracting a growing interest in the research community. In this paper we propose an empirical evaluation of the multi-assignment clustering method using different feature representation models. We define different vectorial representations from both structural egonet information and user profile features. We study and compare the performance on two available labelled Facebook datasets and compare our results with several different baselines. In addition, we provide some insights of the evaluation metrics most commonly used in the literature.

Section: Social Network and Social Circles Detectionmentioning

confidence: 99%

Feature representation for social circles detection using MAC

2016

Neural Comput & Applic

“…Some approaches base the clustering on the network links [10], while others consider the semantic content of social interactions [15]. In between both methodologies, there has also been work on combining the links and the content for doing the clustering [9,12]. Very recently, a new technique studied the characteristics of community structures formed around topical discussion clusters, using modularity maximization algorithms [2].…”

Section: Application To Social Network and Social Circles Detectionmentioning

confidence: 99%

Empirical Evaluation of Different Feature Representations for Social Circles Detection

Pattern Recognition and Image Analysis

2015

Abstract. Social circles detection is a special case of community detection in social network that is currently attracting a growing interest in the research community. We propose in this paper an empirical evaluation of the multi-assignment clustering method using different feature representation models. We define different vectorial representations from both structural egonet information and user profile features. We study and compare the performance on the available labelled Facebook data from the Kaggle competition on learning social circles in networks. We compare our results with several different baselines.

“…Some approaches base the clustering on the network links [2], while others consider the semantic content of social interactions [21]. In between both methodologies, there has also been work on combining the links and the content for doing the clustering [22], [23]. Very recently, a new technique studied the characteristics of community structures formed around topical discussion clusters, using modularity maximization algorithms [24].…”

Section: B Application To Social Network and Social Circles Detectionmentioning

confidence: 99%

Data mapping by Restricted Boltzmann Machines for social circles detection

2015 International Joint Conference on Neural Networks (IJCNN)

2015

Abstract-Social circles detection is a special case of community detection in social network that is currently attracting a growing interest in the research community. In this paper, we propose a two-step technique, making emphasis on the mapping of the data by Restricted Boltzmann Machines (RBMs). Social circles are subsequently inferred by k-means over the preprocessed data. We define different vectorial representations from both structural egonet information and user profile features, and perform a set of tests to adjust the optimal parameters of the RBMs. We study and compare the performance on the ego-Facebook dataset of social circles from Facebook from the Stanford Large Network Dataset Collection. We compare our results with several different baselines.