scholarly journals User Profile Preserving Social Network Embedding

2017 ◽  
Author(s):  
Daokun Zhang ◽  
Jie Yin ◽  
Xingquan Zhu ◽  
Chengqi Zhang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Dimensional Space ◽  
User Profile ◽  
Network Embedding ◽  
Network Nodes ◽  
Profile Information ◽  
Low Dimensional ◽  
Performance Gains

This paper addresses social network embedding, which aims to embed social network nodes, including user profile information, into a latent low-dimensional space. Most of the existing works on network embedding only consider network structure, but ignore user-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, user profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPP-SNE), which incorporates user profile with network structure to jointly learn a vector representation of a social network. The theme of UPP-SNE is to embed user profile information via a nonlinear mapping into a consistent subspace, where network structure is seamlessly encoded to jointly learn informative node representations. Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks.

scholarly journals Balancing User Profile and Social Network Structure for Anchor Link Inferring Across Multiple Online Social Networks

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 12031-12040 ◽  
Author(s):  
Jiangtao Ma ◽  
Yaqiong Qiao ◽  
Guangwu Hu ◽  
Yongzhong Huang ◽  
Meng Wang ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Online Social Networks ◽  
User Profile ◽  
Social Network Structure

scholarly journals Inequality is rising where social network segregation interacts with urban topology

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Gergő Tóth ◽  
Johannes Wachs ◽  
Riccardo Di Clemente ◽  
Ákos Jakobi ◽  
Bence Ságvári ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Urban Geography ◽  
Online Social Network ◽  
Stage Model ◽  
Significant Relationships ◽  
Social Segregation ◽  
Social Network Structure ◽  
Physical Barriers

AbstractSocial networks amplify inequalities by fundamental mechanisms of social tie formation such as homophily and triadic closure. These forces sharpen social segregation, which is reflected in fragmented social network structure. Geographical impediments such as distance and physical or administrative boundaries also reinforce social segregation. Yet, less is known about the joint relationships between social network structure, urban geography, and inequality. In this paper we analyze an online social network and find that the fragmentation of social networks is significantly higher in towns in which residential neighborhoods are divided by physical barriers such as rivers and railroads. Towns in which neighborhoods are relatively distant from the center of town and amenities are spatially concentrated are also more socially segregated. Using a two-stage model, we show that these urban geography features have significant relationships with income inequality via social network fragmentation. In other words, the geographic features of a place can compound economic inequalities via social networks.

scholarly journals Predicting partially observed processes on temporal networks by Dynamics-Aware Node Embeddings (DyANE)

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Koya Sato ◽  
Mizuki Oka ◽  
Alain Barrat ◽  
Ciro Cattuto
Keyword(s):  
Network Structure ◽  
Machine Learning Algorithms ◽  
Temporal Networks ◽  
Dynamical Processes ◽  
Network Nodes ◽  
Partially Observed ◽  
Low Dimensional ◽  
Vector Representations ◽  
Infectious Disease Dynamics ◽  
Random Times

AbstractLow-dimensional vector representations of network nodes have proven successful to feed graph data to machine learning algorithms and to improve performance across diverse tasks. Most of the embedding techniques, however, have been developed with the goal of achieving dense, low-dimensional encoding of network structure and patterns. Here, we present a node embedding technique aimed at providing low-dimensional feature vectors that are informative of dynamical processes occurring over temporal networks – rather than of the network structure itself – with the goal of enabling prediction tasks related to the evolution and outcome of these processes. We achieve this by using a lossless modified supra-adjacency representation of temporal networks and building on standard embedding techniques for static graphs based on random walks. We show that the resulting embedding vectors are useful for prediction tasks related to paradigmatic dynamical processes, namely epidemic spreading over empirical temporal networks. In particular, we illustrate the performance of our approach for the prediction of nodes’ epidemic states in single instances of a spreading process. We show how framing this task as a supervised multi-label classification task on the embedding vectors allows us to estimate the temporal evolution of the entire system from a partial sampling of nodes at random times, with potential impact for nowcasting infectious disease dynamics.

scholarly journals Network Embedding via a Bi-Mode and Deep Neural Network Model

2017 ◽  
Author(s):  
Yang Fang ◽  
Xiang Zhao ◽  
Zhen Tan
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Semantic Information ◽  
Dimensional Space ◽  
Relation Extraction ◽  
Network Embedding ◽  
Structure Information ◽  
Second Mode ◽  
Real World Datasets ◽  
Low Dimensional

Network Embedding (NE) is an important method to learn the representations of network via a low-dimensional space. Conventional NE models focus on capturing the structure information and semantic information of vertices while neglecting such information for edges. In this work, we propose a novel NE model named BimoNet to capture both the structure and semantic information of edges. BimoNet is composed of two parts, i.e., the bi-mode embedding part and the deep neural network part. For bi-mode embedding part, the first mode named add-mode is used to express the entity-shared features of edges and the second mode named subtract-mode is employed to represent the entity-specific features of edges. These features actually reflect the semantic information. For deep neural network part, we firstly regard the edges in a network as nodes, and the vertices as links, which will not change the overall structure of the whole network. Then we take the nodes' adjacent matrix as the input of the deep neural network as it can obtain similar representations for nodes with similar structure. Afterwards, by jointly optimizing the objective function of these two parts, BimoNet could preserve both the semantic and structure information of edges. In experiments, we evaluate BimoNet on three real-world datasets and task of relation extraction, and BimoNet is demonstrated to outperform state-of-the-art baseline models consistently and significantly.

Community detection in complex network by network embedding and density clustering

2021 ◽  
pp. 1-12
Author(s):  
JinFang Sheng ◽  
Huaiyu Zuo ◽  
Bin Wang ◽  
Qiong Li
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Dimensional Space ◽  
Detection Algorithm ◽  
Superior Performance ◽  
Network Embedding ◽  
Detection Algorithms ◽  
Density Clustering ◽  
Community Detection Algorithm ◽  
Low Dimensional

 In a complex network system, the structure of the network is an extremely important element for the analysis of the system, and the study of community detection algorithms is key to exploring the structure of the complex network. Traditional community detection algorithms would represent the network using an adjacency matrix based on observations, which may contain redundant information or noise that interferes with the detection results. In this paper, we propose a community detection algorithm based on density clustering. In order to improve the performance of density clustering, we consider an algorithmic framework for learning the continuous representation of network nodes in a low-dimensional space. The network structure is effectively preserved through network embedding, and density clustering is applied in the embedded low-dimensional space to compute the similarity of nodes in the network, which in turn reveals the implied structure in a given network. Experiments show that the algorithm has superior performance compared to other advanced community detection algorithms for real-world networks in multiple domains as well as synthetic networks, especially when the network data chaos is high.

Simulating Social Network Formation

2011 ◽  
pp. 581-599
Author(s):  
Robert Gilles ◽  
Tabitha James ◽  
Reza Barkhi ◽  
Dimitrios Diamantaras
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Network Formation ◽  
Theoretic Model ◽  
Network Structures ◽  
Agent Behavior ◽  
Graph Theoretic ◽  
Model And Simulation ◽  
Graph Theoretic Models

Social networks depict complex systems as graph theoretic models. The study of the formation of such systems (or networks) and the subsequent analysis of the network structures are of great interest. For information systems research and its impact on business practice, the ability to model and simulate a system of individuals interacting to achieve a certain socio-economic goal holds much promise for proper design and use of cyber networks. We use case-based decision theory to formulate a customizable model of information gathering in a social network. In this model, the agents in the network have limited awareness of the social network in which they operate and of the fixed, underlying payoff structure. Agents collect payoff information from neighbors within the prevailing social network, and they base their networking decisions on this information. Along with the introduction of the decision theoretic model, we developed software to simulate the formation of such networks in a customizable context to examine how the network structure can be influenced by the parameters that define social relationships. We present computational experiments that illustrate the growth and stability of the simulated social networks ensuing from the proposed model. The model and simulation illustrates how network structure influences agent behavior in a social network and how network structures, agent behavior, and agent decisions influence each other.

scholarly journals Head-to-Head Comparison of Social Network Assessments in Stroke Survivors

2020 ◽  
Vol 11 (1) ◽  
pp. 18-24
Author(s):  
Morgan Prust ◽  
Abby Halm ◽  
Simona Nedelcu ◽  
Amber Nieves ◽  
Amar Dhand
Keyword(s):  
Social Support ◽  
Social Networks ◽  
Social Network ◽  
Social Isolation ◽  
Network Structure ◽  
Network Size ◽  
Personal Network ◽  
Mapping Tool ◽  
Social Network Structure ◽  
Network Mapping

Background and Purpose: Social networks influence human health and disease through direct biological and indirect psychosocial mechanisms. They have particular importance in neurologic disease because of support, information, and healthy behavior adoption that circulate in networks. Investigations into social networks as determinants of disease risk and health outcomes have historically relied on summary indices of social support, such as the Lubben Social Network Scale–Revised (LSNS-R) or the Stroke Social Network Scale (SSNS). We compared these 2 survey tools to personal network (PERSNET) mapping tool, a novel social network survey that facilitates detailed mapping of social network structure, extraction of quantitative network structural parameters, and characterization of the demographic and health parameters of each network member. Methods: In a cohort of inpatient and outpatient stroke survivors, we administered LSNS-R, SSNS, and PERSNET in a randomized order to each patient. We used logistic regression to generate correlation matrices between LSNS-R scores, SSNS scores, and PERSNET’s network structure (eg, size and density) and composition metrics (eg, percent kin in network). We also examined the relationship between LSNS-R-derived risk of social isolation with PERSNET-derived network size. Results: We analyzed survey responses for 67 participants and found a significant correlation between LSNS-R, SSNS, and PERSNET-derived indices of network structure. We found no correlation between LSNS-R, SSNS, and PERSNET-derived metrics of network composition. Personal network mapping tool structural and compositional variables were also internally correlated. Social isolation defined by LSNS-R corresponded to a network size of <5. Conclusions: Personal network mapping tool is a valid index of social network structure, with a significant correlation to validated indices of perceived social support. Personal network mapping tool also captures a novel range of health behavioral data that have not been well characterized by previous network surveys. Therefore, PERSNET offers a comprehensive social network assessment with visualization capabilities that quantifies the social environment in a valid and unique manner.

scholarly journals Relation Structure-Aware Heterogeneous Information Network Embedding

2019 ◽  
Vol 33 ◽  
pp. 4456-4463 ◽  
Author(s):  
Yuanfu Lu ◽  
Chuan Shi ◽  
Linmei Hu ◽  
Zhiyuan Liu
Keyword(s):  
Real World ◽  
Dimensional Space ◽  
Structural Characteristics ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Real World Datasets ◽  
Low Dimensional ◽  
Embedding Methods

Heterogeneous information network (HIN) embedding aims to embed multiple types of nodes into a low-dimensional space. Although most existing HIN embedding methods consider heterogeneous relations in HINs, they usually employ one single model for all relations without distinction, which inevitably restricts the capability of network embedding. In this paper, we take the structural characteristics of heterogeneous relations into consideration and propose a novel Relation structure-aware Heterogeneous Information Network Embedding model (RHINE). By exploring the real-world networks with thorough mathematical analysis, we present two structure-related measures which can consistently distinguish heterogeneous relations into two categories: Affiliation Relations (ARs) and Interaction Relations (IRs). To respect the distinctive characteristics of relations, in our RHINE, we propose different models specifically tailored to handle ARs and IRs, which can better capture the structures and semantics of the networks. At last, we combine and optimize these models in a unified and elegant manner. Extensive experiments on three real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods in various tasks, including node clustering, link prediction, and node classification.

scholarly journals Offspring social network structure predicts fitness in families

2012 ◽  
Vol 279 (1749) ◽  
pp. 4914-4922 ◽  
Author(s):  
Nick J. Royle ◽  
Thomas W. Pike ◽  
Philipp Heeb ◽  
Heinz Richner ◽  
Mathias Kölliker
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Conflicts Of Interest ◽  
Parus Major ◽  
Social Structures ◽  
Emergent Properties ◽  
Social Network Structure ◽  
The Social ◽  
The Individual

Social structures such as families emerge as outcomes of behavioural interactions among individuals, and can evolve over time if families with particular types of social structures tend to leave more individuals in subsequent generations. The social behaviour of interacting individuals is typically analysed as a series of multiple dyadic (pair-wise) interactions, rather than a network of interactions among multiple individuals. However, in species where parents feed dependant young, interactions within families nearly always involve more than two individuals simultaneously. Such social networks of interactions at least partly reflect conflicts of interest over the provision of costly parental investment. Consequently, variation in family network structure reflects variation in how conflicts of interest are resolved among family members. Despite its importance in understanding the evolution of emergent properties of social organization such as family life and cooperation, nothing is currently known about how selection acts on the structure of social networks. Here, we show that the social network structure of broods of begging nestling great tits Parus major predicts fitness in families. Although selection at the level of the individual favours large nestlings, selection at the level of the kin-group primarily favours families that resolve conflicts most effectively.

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