Model-Based Method for Social Network Clustering

We propose a simple mixed membership model for social network clustering in this note. A flexible function is adopted to measure affinities among a set of entities in a social network. The model not only allows each entity in the network to possess more than one membership, but also provides accurate statistical inference about network structure. We estimate the membership parameters by using an MCMC algorithm. We evaluate the performance of the proposed algorithm by applying our model to two empirical social network data, the Zachary club data and the bottlenose dolphin network data. We also conduct some numerical studies for different types of simulated networks for assessing the effectiveness of our algorithm. In the end, some concluding remarks and future work are addressed briefly

Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks

Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks, and it has attracted the attention of many researchers in recent years. However, most studies assume that heterogeneous information networks usually follow some simple schemas, such as bi-typed networks or star network schema, and they can only cluster one type of object in the network each time. In this paper, a novel clustering framework is proposed based on sparse tensor factorization for heterogeneous information networks, which can cluster multiple types of objects simultaneously in a single pass without any network schema information. The types of objects and the relations between them in the heterogeneous information networks are modeled as a sparse tensor. The clustering issue is modeled as an optimization problem, which is similar to the well-known Tucker decomposition. Then, an Alternating Least Squares (ALS) algorithm and a feasible initialization method are proposed to solve the optimization problem. Based on the tensor factorization, we simultaneously partition different types of objects into different clusters. The experimental results on both synthetic and real-world datasets have demonstrated that our proposed clustering framework, STFClus, can model heterogeneous information networks efficiently and can outperform state-of-the-art clustering algorithms as a generally applicable single-pass clustering method for heterogeneous network which is network schema agnostic

Neural‑Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding

Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years. Existing works have demonstrated that vertex representation learned through an embedding method provides superior performance in many real-world applications, such as node classification, link prediction, and community detection. However, most of the existing methods for network embedding only utilize topological information of a vertex, ignoring a rich set of nodal attributes (such as user profiles of an online social network, or textual contents of a citation network), which is abundant in all real-life networks. A joint network embedding that takes into account both attributional and relational information entails a complete network information and could further enrich the learned vector representations. In this work, we present Neural-Brane, a novel Neural Bayesian Personalized Ranking based Attributed Network Embedding. For a given network, Neural-Brane extracts latent feature representation of its vertices using a designed neural network model that unifies network topological information and nodal attributes. Besides, it utilizes Bayesian personalized ranking objective, which exploits the proximity ordering between a similar node pair and a dissimilar node pair. We evaluate the quality of vertex embedding produced by Neural-Brane by solving the node classification and clustering tasks on four real-world datasets. Experimental results demonstrate the superiority of our proposed method over the state-of-the-art existing methods

Modeling mobile cellular networks based on social characteristics

Social characteristics have become an important aspect of cellular systems, particularly in next generation networks where cells are miniaturised and social effects can have considerable impacts on network operations. Traffic load demonstrates strong spatial and temporal fluctuations caused by users social activities. In this article, we introduce a new modelling method which integrates the social aspects of individual cells in modelling cellular networks. In the new method, entropy based social characteristics and time sequences of traffic fluctuations are defined as key measures, and jointly evaluated. Spectral clustering techniques can be extended and applied to categorise cells based on these key parameters. Based on the social characteristics respectively, we implement multi-dimensional clustering technologies, and categorize the base stations. Experimental studies are carried out to validate our proposed model, and the effectiveness of the model is confirmed through the consistency between measurements and model. In practice, our modelling method can be used for network planning and parameter dimensioning to facilitate cellular network design, deployments and operations

Modeling Mobile Cellular Networks Based on Social Characteristics

Social characteristics have become an important aspect of cellular systems, particularly in next generation networks where cells are miniaturised and social effects can have considerable impacts on network operations. Traffic load demonstrates strong spatial and temporal fluctuations caused by users social activities. In this article, we introduce a new modelling method which integrates the social aspects of individual cells in modelling cellular networks. In the new method, entropy based social characteristics and time sequences of traffic fluctuations are defined as key measures, and jointly evaluated. Spectral clustering techniques can be extended and applied to categorise cells based on these key parameters. Based on the social characteristics respectively, we implement multi-dimensional clustering technologies, and categorize the base stations. Experimental studies are carried out to validate our proposed model, and the effectiveness of the model is confirmed through the consistency between measurements and model. In practice, our modelling method can be used for network planning and parameter dimensioning to facilitate cellular network design, deployments and operations

Social Network Analysis Using a Multi-agent System: A School System Case

The quality of k-12 education has been a major concern in the nation for years. School systems, just like many other social networks, appear to have a hierarchical structure. Understanding this structure could be the key to better evaluate student performance and improve school quality. Many researches have been focusing on detecting hierarchical structure by using hierarchical clustering algorithms. Compared to existing methods, we design an interaction-based similarity measure to accomplish hierarchical clustering in order to detect hierarchical structures in social networks (e.g. school district networks). This method uses a Multi-agent System for it is based on agent interactions. With the network structure detected, we also build a model, which is inspired by the MAXQ algorithm, to decompose funding policy task into subtask and then evaluate these subtasks by using funding distribution policies from past years and looking for possible relationships between student performances and funding policies. For experiment, we use real school data from Bexar county’s 15 school districts. The first result shows that our interaction based method is able to generate meaningful clustering and dendrogram for social networks. And our policy evaluation model is able to evaluate funding policies from past three years in Bexar County and conclude that increasing funding does not necessarily have a positive impact on student performance and it is generally not the case that the more spend the better