Multiple Types Of Relationships Research Articles

Tensor Decomposition for Multilayer Networks Clustering

Clustering on multilayer networks has been shown to be a promising approach to enhance the accuracy. Various multilayer networks clustering algorithms assume all networks derive from a latent clustering structure, and jointly learn the compatible and complementary information from different networks to excavate one shared underlying structure. However, such an assumption is in conflict with many emerging real-life applications due to the existence of noisy/irrelevant networks. To address this issue, we propose Centroid-based Multilayer Network Clustering (CMNC), a novel approach which can divide irrelevant relationships into different network groups and uncover the cluster structure in each group simultaneously. The multilayer networks is represented within a unified tensor framework for simultaneously capturing multiple types of relationships between a set of entities. By imposing the rank-(Lr,Lr,1) block term decomposition with nonnegativity, we are able to have well interpretations on the multiple clustering results based on graph cut theory. Numerically, we transform this tensor decomposition problem to an unconstrained optimization, thus can solve it efficiently under the nonlinear least squares (NLS) framework. Extensive experimental results on synthetic and real-world datasets show the effectiveness and robustness of our method against noise and irrelevant data.

Assessing the computational complexity of multilayer subgraph detection

AbstractMultilayer graphs consist of several graphs, called layers, where the vertex set of all layers is the same but each layer has an individual edge set. They are motivated by real-world problems where entities (vertices) are associated via multiple types of relationships (edges in different layers). We chart the border of computational (in)tractability for the class of subgraph detection problems on multilayer graphs, including fundamental problems such as maximum-cardinality matching, finding certain clique relaxations, or path problems. Mostly encountering hardness results, sometimes even for two or three layers, we can also spot some islands of computational tractability.

Couples’ Sexual Communication and Dimensions of Sexual Function: A Meta-Analysis

Research and clinical data have shown that couples with sexual problems report a lack of sexual communication. However, no published meta-analyses have evaluated the relationship between sexual communication and sexual function. This meta-analysis examines the correlation between couples’ sexual communication and dimension of sexual function across 48 studies. Sexual communication was positively associated with sexual desire (r = .16), sexual arousal (r = .21), lubrication (r = .17), orgasm (r = .23), erectile function (r = .19), less pain (r = .12), and overall sexual function (r = .35). The effect sizes for sexual desire (r = .21; r = .12) and orgasm (r = .26; r = .16) were higher for women than for men. For overall sexual function, studies with married participants (r = .47) had a larger effect size than studies with participants with multiple relationship types (r = .31) or than studies with dating participants (r = .11). Effect sizes were larger for studies conducted outside of the United States (r = .39) compared to studies conducted in the United States (r = .12). We discuss the importance of addressing the relationship between sexual communication and sexual function, as well as future directions for research in this area.

Conserved Disease Modules Extracted From Multilayer Heterogeneous Disease and Gene Networks for Understanding Disease Mechanisms and Predicting Disease Treatments.

Disease relationship studies for understanding the pathogenesis of complex diseases, diagnosis, prognosis, and drug development are important. Traditional approaches consider one type of disease data or aggregating multiple types of disease data into a single network, which results in important temporal- or context-related information loss and may distort the actual organization. Therefore, it is necessary to apply multilayer network model to consider multiple types of relationships between diseases and the important interplays between different relationships. Further, modules extracted from multilayer networks are smaller and have more overlap that better capture the actual organization. Here, we constructed a weighted four-layer disease-disease similarity network to characterize the associations at different levels between diseases. Then, a tensor-based computational framework was used to extract Conserved Disease Modules (CDMs) from the four-layer disease network. After filtering, nine significant CDMs were reserved. The statistical significance test proved the significance of the nine CDMs. Comparing with modules got from four single layer networks, CMDs are smaller, better represent the actual relationships, and contain potential disease-disease relationships. KEGG pathways enrichment analysis and literature mining further contributed to confirm that these CDMs are highly reliable. Furthermore, the CDMs can be applied to predict potential drugs for diseases. The molecular docking techniques were used to provide the direct evidence for drugs to treat related disease. Taking Rheumatoid Arthritis (RA) as a case, we found its three potential drugs Carvedilol, Metoprolol, and Ramipril. And many studies have pointed out that Carvedilol and Ramipril have an effect on RA. Overall, the CMDs extracted from multilayer networks provide us with an impressive understanding disease mechanisms from the perspective of multi-layer network and also provide an effective way to predict potential drugs for diseases based on its neighbors in a same CDM.

Correlation constraint shortest path over large multi-relation graphs

Multi-relation graphs intuitively capture the heterogeneous correlations among real-world entities by allowing multiple types of relationships to be represented as entity-connecting edges, i.e., two entities could be correlated with more than one type of relationship. This is important in various applications such as social network analysis, ecology, and bio-informatics. Existing studies on these graphs usually consider an edge label constraint perspective, where each edge contains only one label and each edge is considered independently. For example, there are lines of research focusing on reachability between two vertices under a set of edge label constraints, or finding paths whose consecutive edge labels satisfy a user-specified logical expression. This is too restricted in real graphs, and in this work, we define a generic correlation constraint on multi-relation graphs from the perspective of vertex correlations, where a correlation can be defined recursively. Specifically, we formalize and investigate the shortest path problem over large multi-relation graphs in the presence of both necessity and denial constraints, which have various real applications. We show that it is nontrivial to apply conventional graph traversal algorithms (e.g., BFS or DFS) to address the challenge. To effectively reduce the search space, we propose a Hybrid Relation Encoding method, a.k.a. HyRE, to encode both topological and relation information in a compact way. We conduct extensive experiments over large real-world graphs to validate the effectiveness and efficiency of the proposed solution.

Multi-task network embedding

As there are various data mining applications involving network analysis, network embedding is frequently employed to learn latent representations or embeddings that encode the network structure. However, existing network embedding models are only designed for a single network scenario. It is common that nodes can have multiple types of relationships in big data era, which results in multiple networks, e.g., multiple social networks and multiple gene regulatory networks. Jointly embedding multiple networks thus may make network-specific embeddings more comprehensive and complete as the same node may expose similar or complementary characteristics in different networks. In this paper, we thus propose an idea of multi-task network embedding to jointly learn multiple network-specific embeddings for each node via enforcing an extra information-sharing embedding. We instantiate the idea in two types of models that are different in the mechanism for enforcing the information-sharing embedding. The first type enforces the information-sharing embedding as a common embedding shared by all tasks, which is similar to the concept of the common metric in multi-task metric learning, while the second type enforces the information-sharing embedding as a consensus embedding on which all network-specific embeddings agree. Moreover, we propose two mechanisms for embedding the network structure, which are first-order proximity preserving and second-order proximity preserving. We demonstrate through comprehensive experiments on three real-world datasets that the proposed models outperform recent network embedding models in applications including visualization, link prediction, and multi-label classification.

Detect potential relations by link prediction in multi-relational social networks

Potential relation detecting on social network has become more important for decision making in many business disciplines, such as marketing, business strategy, human resources development, finance planning, business transformation, insurance policy design, and tourism management. People are used to seeking useful information from the relationships among social members to support their decisions on investment, partner seeking and marketing. Corporations are seeking opportunities to leverage them for “word of mouth” advertising based on the relations between the customers. When we collect and observe relationships between people, missing or redundant relations unavoidably occur since the time and cost restrictions in market or social investigation prevent us to discover all the relations. Moreover, since the social relations are changing constantly, current social relations may disappear, and new relations will be established. Many trade and social networks consist of multiple types of relations between the individuals. This paper presents an efficient method to detect the potential and future social relations between individuals in multi-relational social networks using link prediction. First, we calculate the belief of each individual by belief propagation on each type of relations. Based on the belief vectors, the similarities between various types of relations are computed to measure their mutual influence. Based on the similarities between various types of relations, we model link prediction as the problem of matrix completion by optimizing its max-norm constrained formulation. We propose a projected gradient descent optimization algorithm which is scalable to large size networks. Empirical results on real multi-relational social networks demonstrate that the predicting results of our algorithm have higher quality compared with other similar algorithms.

Interactive Discovery of Coordinated Relationship Chains with Maximum Entropy Models

Modern visual analytic tools promote human-in-the-loop analysis but are limited in their ability to direct the user toward interesting and promising directions of study. This problem is especially acute when the analysis task is exploratory in nature, e.g., the discovery of potentially coordinated relationships in massive text datasets. Such tasks are very common in domains like intelligence analysis and security forensics where the goal is to uncover surprising coalitions bridging multiple types of relations. We introduce new maximum entropy models to discover surprising chains of relationships leveraging count data about entity occurrences in documents. These models are embedded in a visual analytic system called MERCER (Maximum Entropy Relational Chain ExploRer) that treats relationship bundles as first class objects and directs the user toward promising lines of inquiry. We demonstrate how user input can judiciously direct analysis toward valid conclusions, whereas a purely algorithmic approach could be led astray. Experimental results on both synthetic and real datasets from the intelligence community are presented.

Query-Focused Personalized Citation Recommendation With Mutually Reinforced Ranking

Many state-of-the-art citation recommendation methods have been proposed for finding a list of reference papers for a given manuscript, among which the graph-based method has gained particular attention, due to its flexibility for incorporating various information that embodies user’s preferences. To achieve a more synthetic, accurate, and personalized recommendation result than the previous graph-based methods, this paper proposes a new graph-based recommendation framework that exploiting diversified link information in a bibliographic network and the concise query information that embodies the specific requirement of user comprehensively. The proposed framework not only performs mutual reinforcement rules on all available multiple types of relations in a multi-layered graph but also incorporates the query information into the multi-layered mutual reinforcement schema to construct a multi-layered mutually reinforced query-focused (MMRQ) citation recommendation approach. Extensive experiments have been conducted on a subset of anthology network data set. Experimental results of Recall measures, normalized discounted cumulative gain measures, and case study all demonstrate that our MMRQ method obtains a superior citation recommendation.

LATENT SPACE MODELS FOR MULTIVIEW NETWORK DATA.

Social relationships consist of interactions along multiple dimensions. In social networks, this means that individuals form multiple types of relationships with the same person (e.g., an individual will not trust all of his/her acquaintances). Statistical models for these data require understanding two related types of dependence structure: (i) structure within each relationship type, or network view, and (ii) the association between views. In this paper, we propose a statistical framework that parsimoniously represents dependence between relationship types while also maintaining enough flexibility to allow individuals to serve different roles in different relationship types. Our approach builds on work on latent space models for networks [see, e.g., J. Amer. Statist. Assoc.97 (2002) 1090-1098]. These models represent the propensity for two individuals to form edges as conditionally independent given the distance between the individuals in an unobserved social space. Our work departs from previous work in this area by representing dependence structure between network views through a multivariate Bernoulli likelihood, providing a representation of between-view association. This approach infers correlations between views not explained by the latent space model. Using our method, we explore 6 multiview network structures across 75 villages in rural southern Karnataka, India [Banerjee et al. (2013)].

Connecting the dots: A longitudinal observation of relief organizations' representational networks on social media

In response to the call in the public relations research to examine organizations' relationship building beyond dyadic organization-public ties, this study employs a multidimensional network approach to understand relief organizations' relationship building through different features of social media. Unlike the organizations studied in the existing public relations research, relief organizations often incorporate emergency or disaster response as part of their mission operations. Specifically, this study focuses on the organizations involved in the response actions after Typhoon Haiyan. Analysis of 66 relief organizations' network data on Twitter and Facebook shows that organizations' multiple types of relationships in the form of interorganizational representational networks constructed through different features of social media are closely linked during and immediately after Typhoon Haiyan. However, organizations' positional centrality stays persistent across these networks over time, which suggests both constraints and opportunities for organizations to build relational advantages through distinct features of social media. Findings of this study show the usefulness of a multidimensional network approach in revealing the hidden patterns behind organizations' communication behavior on social media. In doing so, this study offers more comprehensive explanations for organizations' relationship building and relational outcomes through social media use.

Discovering suspicious behavior in multilayer social networks

Discovering suspicious and illicit behavior in social networks is a significant problem in social network analysis. The patterns of interactions of suspicious users are quite different from their peers and can be identified by using anomaly detection techniques. The existing anomaly detection techniques on social networks focus on networks with only one type of interaction among the users. However, human interactions are inherently multiplex in nature with multiple types of relationships existing among the users, leading to the formation of multilayer social networks. In this paper, we investigate the problem of anomaly detection on multilayer social networks by combining the rich information available in multiple network layers. We propose a pioneer approach namely ADOMS (Anomaly Detection On Multilayer Social networks), an unsupervised, parameter-free, and network feature-based methodology, that automatically detects anomalous users in a multilayer social network and rank them according to their anomalousness. We consider the two well-known anomalous patterns of clique/near-clique and star/near-star anomalies in social networks, and users are ranked according to the degree of similarity of their neighborhoods in different layers to stars or cliques. Experimental results on several real-world multilayer network datasets demonstrate that our approach can effectively detect anomalous nodes in multilayer social networks.

The psychological costs of social support imbalance: Variation across relationship context and age.

Psychological well-being benefits of receiving social support are well-established. Growing evidence also suggests parallel benefits of giving support. However, much less attention has been given to understanding the psychological correlates of imbalance in giving and receiving social support. We examined associations between social support (given, received, and imbalance) and psychological well-being in multiple relationship types (friends, family, and spouse). Greater levels of both receiving and giving social support were independently associated with more favorable psychological well-being, while imbalance in the ratio of support given and received was associated with poorer psychological well-being. Findings varied between relationship types and across age.

Dynamics of Large Multi-View Social Networks: Synergy, Cannibalization and Cross-View Interplay.

Most social networking services support multiple types of relationships between users, such as getting connected, sending messages, and consuming feed updates. These users and relationships can be naturally represented as a dynamic multi-view network, which is a set of weighted graphs with shared common nodes but having their own respective edges. Different network views, representing structural relationship and interaction types, could have very distinctive properties individually and these properties may change due to interplay across views. Therefore, it is of interest to study how multiple views interact and affect network dynamics and, in addition, explore possible applications to social networking. In this paper, we propose approaches to capture and analyze multi-view network dynamics from various aspects. Through our proposed descriptors, we observe the synergy and cannibalization between different user groups and network views from LinkedIn dataset. We then develop models that consider the synergy and cannibalization per new relationship, and show the outperforming predictive capability of our models compared to baseline models. Finally, the proposed models allow us to understand the interplay among different views where they dynamically change over time.

An Access Control Model for Online Social Networks Using User-to-User Relationships

Users and resources in online social networks (OSNs) are interconnected via various types of relationships. In particular, user-to-user relationships form the basis of the OSN structure, and play a significant role in specifying and enforcing access control. Individual users and the OSN provider should be enabled to specify which access can be granted in terms of existing relationships. In this paper, we propose a novel user-to-user relationship-based access control (UURAC) model for OSN systems that utilizes regular expression notation for such policy specification. Access control policies on users and resources are composed in terms of requested action, multiple relationship types, the starting point of the evaluation, and the number of hops on the path. We present two path checking algorithms to determine whether the required relationship path between users for a given access request exists. We validate the feasibility of our approach by implementing a prototype system and evaluating the performance of these two algorithms.

Measuring the social influences of scientist groups based on multiple types of collaboration relations

Scientists often collaborate with each other and may produce social influences through their collaboration on scientific activities. While the subject of ranking scientists has received significant attention in previous studies, these studies often examine the social influences of individual scientists and are based on the assumption that all collaborations between scientists are of the same type. However, these two limitations do not match real scientific collaborations. Currently, scientists are often associated with groups in which the scientists always study related research topics or have close collaboration relationships. Moreover, current scientists often collaborate through multiple relationship types, and different types of relationships may have different effects on the social influences of the scientists. To solve these two problems, this paper presents a model that measures the social influences of scientist groups based on multiple types of collaboration relationships. The model addresses two general group types (hierarchical and nonhierarchical) and two general collaboration situations: one is that the multiplex collaboration relationships are independent, and the other is that the multiplex collaboration relationships are correlated. The model mainly adopts the linear weighted sum methodology, which can make the time complexities of key algorithms low. Finally, we create some case studies and make experiments to demonstrate the effectiveness of our model.

Modeling customer preferences using multidimensional network analysis in engineering design

Motivated by overcoming the existing utility-based choice modeling approaches, we present a novel conceptual framework of multidimensional network analysis (MNA) for modeling customer preferences in supporting design decisions. In the proposed multidimensional customer–product network (MCPN), customer–product interactions are viewed as a socio-technical system where separate entities of ‘customers’ and ‘products’ are simultaneously modeled as two layers of a network, and multiple types of relations, such as consideration and purchase, product associations, and customer social interactions, are considered. We first introduce a unidimensional network where aggregated customer preferences and product similarities are analyzed to inform designers about the implied product competitions and market segments. We then extend the network to a multidimensional structure where customer social interactions are introduced for evaluating social influence on heterogeneous product preferences. Beyond the traditional descriptive analysis used in network analysis, we employ the exponential random graph model (ERGM) as a unified statistical inference framework to interpret complex preference decisions. Our approach broadens the traditional utility-based logit models by considering dependency among complex customer–product relations, including the similarity of associated products, ‘irrationality’ of customers induced by social influence, nested multichoice decisions, and correlated attributes of customers and products.

Consistent community detection in multi-relational data through restricted multi-layer stochastic blockmodel

In recent years there has been an increased interest in statistical analysis of data with multiple types of relations among a set of entities. Such multi-relational data can be represented as multi-layer graphs where the set of vertices represents the entities and multiple types of edges represent the different relations among them. For community detection in multi-layer graphs, we consider two random graph models, the multi-layer stochastic blockmodel (MLSBM) and a model with a restricted parameter space, the restricted multi-layer stochastic blockmodel (RMLSBM). We derive consistency results for community assignments of the maximum likelihood estimators (MLEs) in both models where MLSBM is assumed to be the true model, and either the number of nodes or the number of types of edges or both grow. We compare MLEs in the two models with other baseline approaches, such as separate modeling of layers, aggregating the layers and majority voting. RMLSBM is shown to have advantage over MLSBM when either the growth rate of the number of communities is high or the growth rate of the average degree of the component graphs in the multi-graph is low. We also derive minimax rates of error and sharp thresholds for achieving consistency of community detection in both models, which are then used to compare the multi-layer models with a baseline model, the aggregate stochastic block model. The simulation studies and real data applications confirm the superior performance of the multi-layer approaches in comparison to the baseline procedures.

Bioinformatics Knowledge Map for Analysis of Beta-Catenin Function in Cancer.

Given the wealth of bioinformatics resources and the growing complexity of biological information, it is valuable to integrate data from disparate sources to gain insight into the role of genes/proteins in health and disease. We have developed a bioinformatics framework that combines literature mining with information from biomedical ontologies and curated databases to create knowledge “maps” of genes/proteins of interest. We applied this approach to the study of beta-catenin, a cell adhesion molecule and transcriptional regulator implicated in cancer. The knowledge map includes post-translational modifications (PTMs), protein-protein interactions, disease-associated mutations, and transcription factors co-activated by beta-catenin and their targets and captures the major processes in which beta-catenin is known to participate. Using the map, we generated testable hypotheses about beta-catenin biology in normal and cancer cells. By focusing on proteins participating in multiple relation types, we identified proteins that may participate in feedback loops regulating beta-catenin transcriptional activity. By combining multiple network relations with PTM proteoform-specific functional information, we proposed a mechanism to explain the observation that the cyclin dependent kinase CDK5 positively regulates beta-catenin co-activator activity. Finally, by overlaying cancer-associated mutation data with sequence features, we observed mutation patterns in several beta-catenin PTM sites and PTM enzyme binding sites that varied by tissue type, suggesting multiple mechanisms by which beta-catenin mutations can contribute to cancer. The approach described, which captures rich information for molecular species from genes and proteins to PTM proteoforms, is extensible to other proteins and their involvement in disease.

Clustering network layers with the strata multilayer stochastic block model.

Multilayer networks are a useful data structure for simultaneously capturing multiple types of relationships between a set of nodes. In such networks, each relational definition gives rise to a layer. While each layer provides its own set of information, community structure across layers can be collectively utilized to discover and quantify underlying relational patterns between nodes. To concisely extract information from a multilayer network, we propose to identify and combine sets of layers with meaningful similarities in community structure. In this paper, we describe the "strata multilayer stochastic block model" (sMLSBM), a probabilistic model for multilayer community structure. The central extension of the model is that there exist groups of layers, called "strata", which are defined such that all layers in a given stratum have community structure described by a common stochastic block model (SBM). That is, layers in a stratum exhibit similar node-to-community assignments and SBM probability parameters. Fitting the sMLSBM to a multilayer network provides a joint clustering that yields node-to-community and layer-to-stratum assignments, which cooperatively aid one another during inference. We describe an algorithm for separating layers into their appropriate strata and an inference technique for estimating the SBM parameters for each stratum. We demonstrate our method using synthetic networks and a multilayer network inferred from data collected in the Human Microbiome Project.