Role analysis for profile generation in heterogeneous social network

Accurate user representation learning has been proven fundamental for many social media applications, including community detection, recommendation, etc. A major challenge lies in that, the available data in a single social network are usually very limited and sparse. In real life, many people are members of several social networks in the same time. Constrained by the features and design of each, any single social platform offers only a partial view of a user from a particular perspective. In this paper, we propose MV-URL, a multi-view user representation learning model to enhance user modeling by integrating the knowledge from various networks. Different from the traditional network embedding frameworks where either the whole framework is single-network based or each network involved is a homogeneous network, we focus on multiple social networks and each network in our task is a heterogeneous network. It's very challenging to effectively fuse knowledge in this setting as the fusion depends upon not only the varying relatedness of information sources, but also the target application tasks. MV-URL focuses on two tasks: user account linkage (i.e., to predict the missing true user account linkage across social media) and user attribute prediction. Extensive evaluations have been conducted on two real-world collections of linked social networks, and the experimental results show the superiority of MV-URL compared with existing state-of-art embedding methods. It can be learned online, and is trivially parallelizable. These qualities make it suitable for real world applications.

RA-HGNN: Attribute completion of heterogeneous graph neural networks based on residual attention mechanism

Most Americans are sorted into social networks that are largely politically homogeneous. A large body of political science research has explored the behavioral implications of being embedded in a politically homogeneous or heterogeneous network, but substantially less attention has been given to explaining why some people find themselves in politically homogeneous or heterogeneous social networks. In this article, we explore the psychological and physiological underpinnings of political network homogeneity. We use social network data from an original survey of 129 undergraduates paired with lab experimental evidence that measures individuals' physiological reactivity to an anticipated political discussion. Using our original survey and a separate nationally representative survey, we find suggestive evidence that individuals who are more socially anxious are more likely to share partisanship with their social network ties. Moreover, we find that individuals who experienced a greater increase in heart rate when anticipating a political discussion were more likely to be in homogeneous discussion networks, but we do not find a relationship between electrodermal activity and network homogeneity. Aversion to psychological and physiological discomfort induced by political discussions could contribute to social polarization in the American public.

Harnessing heterogeneous social networks for better recommendations: A grey relational analysis approach

A relation-aware heterogeneous graph convolutional network for relationship prediction

At present, the improved social network analysis methods are mainly based on homogeneous networks. However, the actual social networks are heterogeneous in nature. Heterogeneous social networks can better reflect the composition of the system and the associated relationships than the homogeneous social network model. The previously proposed ranking clustering provides a new idea, however, the algorithm only completes the clustering results of specific target types and cannot cover the complete heterogeneous network type. By introducing collaborative clustering algorithm and combining it with ranking, we propose a ranking collaborative clustering algorithm. Firstly, based on ranking clustering, the ranking distribution matrix is obtained, and then collaborative clustering is used to complete different types of clustering, and the relationship between different types and the same types is fully utilized. The experimental results on the real twitter and foursquare datasets show that the ranking collaborative clustering algorithm has better performance on the modularity compared to the ranking clustering.

Heterogeneous social networks, which are characterized by diverse interaction types, have resulted in new challenges for missing link prediction. Most deep learning models tend to capture type-specific features to maximize the prediction performances on specific link types. However, the types of missing links are uncertain in heterogeneous social networks; this restricts the prediction performances of existing deep learning models. To address this issue, we propose a multi-type transferable method ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MTTM</i> ) for missing link prediction in heterogeneous social networks, which exploits adversarial neural networks to remain robust against type differences. It comprises a generative predictor and a discriminative classifier. The generative predictor can extract link representations and predict whether the unobserved link is a missing link. To generalize well for different link types to improve the prediction performance, it attempts to deceive the discriminative classifier by learning transferable feature representations among link types. In order not to be deceived, the discriminative classifier attempts to accurately distinguish link types, which indirectly helps the generative predictor judge whether the learned feature representations are transferable among link types. Finally, the integrated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MTTM</i> is constructed on this minimax two-player game between the generative predictor and discriminative classifier to predict missing links based on transferable feature representations among link types. Extensive experiments show that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MTTM</i> can outperform state-of-the-art baselines for missing link prediction in heterogeneous social networks.

Real-world complex networks are inherently heterogeneous; they have different types of nodes, attributes, and relationships. In recent years, various methods have been proposed to automatically learn how to encode the structural and semantic information contained in heterogeneous information networks (HINs) into low-dimensional embeddings; this task is called heterogeneous network embedding (HNE). Efficient HNE techniques can benefit various HIN-based machine learning tasks such as node classification, recommender systems, and information retrieval. Here, we provide a comprehensive survey of key advancements in the area of HNE. First, we define an encoder-decoder-based HNE model taxonomy. Then, we systematically overview, compare, and summarize various state-of-the-art HNE models and analyze the advantages and disadvantages of various model categories to identify more potentially competitive HNE frameworks. We also summarize the application fields, benchmark datasets, open source tools, and performance evaluation in the HNE area. Finally, we discuss open issues and suggest promising future directions. We anticipate that this survey will provide deep insights into research in the field of HNE.

People often express opinions towards others in a social network, causing sentiment links to form among users. To develop effective methods for discovering implicit sentiment links among users, the extraction and exploitation of structural semantic information from heterogeneous social networks are of great importance. We propose a novel heterogeneous social network embedding-based approach for sentiment link prediction that takes both global structural information with multi-dimensional relations and heterogeneous context information into consideration to leverage rich and intrinsic association information. Specifically, the attributed heterogeneous social network and Sentic LSTM-based sentiment link network are employed to incorporate various explicit context knowledge and implicit multi-dimensional user interaction association knowledge into representation learning and sentiment link prediction. The experimental results on a real-world dataset show that the proposed approach has advantages over the state-of-the-art baselines. The results show the effectiveness of incorporating social relations and profile context information into sentiment link prediction, especially in cold-start scenarios. The learned embedding representation that incorporates both structural information with multi-dimensional relations and context information from heterogeneous social networks can improve sentiment link prediction performance. The proposed approach is effective and feasible for detecting unobserved sentiment links from online social networks and outperforms the state-of-the-art baselines in sentiment link prediction tasks.

Social network analysis has typically concerned analysis of one type of tie connecting nodes of the same type. It has however been recognised that people are connected through multiple types of ties and that people in addition are affiliated with multiple types of non-people nodes. Exponential random graph models (ERGM) is a family of statistical models for social networks that at this point allows for a number of different types of network data, including one-mode networks, bipartite networks, multiplex data, as well as multilevel network data. Multilevel networks have been proposed as a joint representation of associations between multiple types of entities or nodes, such as people and organization, where two types of nodes gives rise to three distinct types of ties. The typical roster data collection method may be impractical or infeasible when the node sets are hard to detect or define or because of the cognitive demands on respondents. Multilevel multilayered networks allow us to consider a multitude of different sources of data and to sample on different types of nodes and relations. We consider modelling multilevel multilayered networks using exponential random graph models and extend a recently developed Bayesian data-augmentation scheme to allow for partially missing data. We illustrate the proposed inference procedures for the case of multilevel snowball sampling and sampling with error based on the Noordin Top network.

We study the dynamics of social networks in terms of population growth and control of user behavior. Most of the current research in social networks focus on static analysis through graph theoretic models to represent the networks or focus on modeling the traffic. Here, we study the cost of collaborative vs individualistic behavior of users in order to grow their network size in a social network. Each user incurs a cost (monetary or emotional) for collaboration. We formulate the behavior of the users as a non-linear optimization problem with a cost. The objective function of the optimization problem is obtained using a stochastic analysis of population growth in social networks, based on the first-passage time of a birth-death process. The stochastic model is validated by comparison with real data obtained from Twitter Results indicate that a homogeneous social network (in which users have similar characteristics) will be individualistic. However, heterogeneous social networks (users with different characteristics) exhibit a threshold effect, i.e., there is a minimum cost, below which the network is as collaborative as desired and a maximum cost above which the network is individualistic as required. To the best of our knowledge, this is one of the first analysis of dynamics of user behavior and temporal population growth in social networks.

Using a cross-sectional survey of two sampled counties in Kenya, this article analyses whether social networking sites reflect social network heterogeneity. It then examines how social network heterogeneity influences polarisation in Kenya. Three types of polarisation are examined: party, ethnic and ideological (around county resources). The study focuses specifically on the public WhatsApp platform (the most popular SNS in Kenya). To assess this empirical data, theoretical perspectives are drawn from the literatures on incidental and selective exposure and their impact on political polarisation. The findings indicate that the randomised composition of a WhatsApp group through public links indeed reflects social network heterogeneity. The findings further show that posting political news, obtaining political news and commenting on political news influence social network heterogeneity to some degree. This social network heterogeneity was also found to influence all three (party, ideological and ethnic) types of polarisation in varying but significant degrees.

In the recent years, researchers have developed a surge of interest in the identification and analysis of the communities in complex heterogeneous social networks. Mining latent community structure in such networks is an arduous task owing to the multiple relations among the users. In this article, a multi-relational network is modeled as a tensor and then tensor factorization is performed to obtain the hidden features of the data. Based on the revealed inherent features of the data, clustering is applied to discover topically meaningful communities. The purpose of this study is to comparatively investigate the performance of various tensor factorization and clustering techniques. Experimental evaluation is conducted on synthetic as well as real world datasets and statistical tests are conducted to further validate the results.

In recent years, a new wave of networks labelled Wireless Sensor Networks (WSNs) has attracted a lot of attention from researchers in both academic and industrial communities. A WSN consists of a collection of sensor nodes and a base station connected through wireless channels, and can be used for many applications such as military application, building distributed systems, physical environment monitoring, and security surveillance among others. A big advantage of sensor networks is represented by ease of deployment, reducing installation cost, possibility to distribute the tiny sensors over a wide region, and larger fault tolerance (V. Loscri et al., 2005). However, despite the infinite scopes of wireless sensor networks applications, they are limited by the node battery lifetime. Such constraints combined with a typical deployment of large number of sensor nodes have posed many challenges to the design and management of sensor networks and necessitate energyawareness at all layers of the networking protocol stack (Q. Xue & A. Ganz, 2004). Therefore, energy efficient algorithms have been one of the most challenging issues for WSNs. Sensor nodes can be in one of four states, namely transmit, receive, idle and sleep. The largest part of a node’s energy is consumed while transmitting and receiving. Minimizing the number of communications by eliminating or aggregating redundant sensed data saves much amount of energy (L. B. Ruiz et al., 2003). Among these clustering sensor networks are a very attractive approach because clustering allows for scalability, data aggregation, and energy efficiency. In a clustering network, nodes are grouped into clusters and there are special nodes called cluster head. They are responsible for an efficient way to lower energy consumption within a cluster by performing data aggregation. In a heterogeneous sensor network, two or more different types of nodes with different battery energy and functionality are used. On the other hand, in homogeneous networks all the sensor nodes are identical in terms of battery energy and hardware complexity. As a result, network performance decreases since the cluster head nodes goes down before other nodes do. Thus dynamic, energy efficient and adaptive cluster head selection algorithm is very important. Sensor networks can be divided in two classes as event-driven and continuous dissemination networks according to the periodicity of communication (L. B. Ruiz et al., 9

The online social network alignment problem aims at inferring the anchor links connecting the shared users across social networks, which are usually subject to the one-to-one cardinality constraint. Several existing social network alignment models have been proposed, many of which are based on the supervised learning setting. Given a set of labeled anchor links, a group of features can be extracted manually for the anchor links to build these models. Meanwhile, such methods may encounter great challenges in the application on real-world social network datasets, since manual feature extraction can be extremely expensive and tedious for the social networks involving heterogeneous information. In this paper, we propose to address the heterogeneous social network alignment problem with a deep learning model, namely DETA (Deep nETwork Alignment). Besides a small number of explicit features, DETA can automatically learn a set of latent features from the heterogeneous information. DETA models the anchor link one-to-one cardinality constraint as a mathematical constraint on the node degrees. Extensive experiments have been done on real-world aligned heterogeneous social network datasets, and the experimental results have demonstrated the effectiveness of the proposed model compared against the existing state-of-the-art baseline methods.