scholarly journals Online Social Networks (OSN) Evolution Model Based on Homophily and Preferential Attachment

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 654 ◽  
Author(s):  
Jebran Khan ◽  
Sungchang Lee
Keyword(s):  
Social Networks ◽  
Structural Properties ◽  
Online Social Networks ◽  
Large Scale ◽  
Preferential Attachment ◽  
Real Life ◽  
Synthetic Data ◽  
Evolution Model ◽  
Scale Invariant ◽  
Scale Free

In this paper, we propose a new scale-free social networks (SNs) evolution model that is based on homophily combined with preferential attachments. Our model enables the SN researchers to generate SN synthetic data for the evaluation of multi-facet SN models that are dependent on users’ attributes and similarities. Homophily is one of the key factors for interactive relationship formation in SN. The synthetic graph generated by our model is scale-invariant and has symmetric relationships. The model is dynamic and sustainable to changes in input parameters, such as number of nodes and nodes’ attributes, by conserving its structural properties. Simulation and evaluation of models for large-scale SN applications need large datasets. One way to get SN data is to generate synthetic data by using SN evolution models. Various SN evolution models are proposed to approximate the real-life SN graphs in previous research. These models are based on SN structural properties such as preferential attachment. The data generated by these models is suitable to evaluate SN models that are structure dependent but not suitable to evaluate models which depend on the SN users’ attributes and similarities. In our proposed model, users’ attributes and similarities are utilized to synthesize SN graphs. We evaluated the resultant synthetic graph by analyzing its structural properties. In addition, we validated our model by comparing its measures with the publicly available real-life SN datasets and previous SN evolution models. Simulation results show our resultant graph to be a close representation of real-life SN graphs with users’ attributes.

Identification of Active Valuable Nodes in Temporal Online Social Network with Attributes

2014 ◽  
Vol 13 (04) ◽  
pp. 839-864 ◽  
Author(s):  
Dehong Qiu ◽  
Hao Li ◽  
Yuan Li
Keyword(s):  
Social Networks ◽  
Structural Properties ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Large Scale ◽  
Online Social Network ◽  
Large Fraction ◽  
Great Expectations ◽  
Spatio Temporal ◽  
Behavioral Attributes

The rapidly growing online social networks have generated great expectations connected with their potential business values. The aim of this paper is to identify the active valuable nodes that can spread business information to a large fraction of the individuals in large-scale temporal online social networks as quickly as possible. Most studies focus on static social networks, the study on the identification of active valuable nodes in temporal online social networks with quantitative attributes is still young. In this paper, we propose a method to identify active valuable nodes based on their static structural properties and temporal behavioral attributes. The method first chooses the candidates of the active valuable nodes by the static analysis of their structural properties. Then, the candidate's behavioral trend is extracted from its activity records. Through analyzing the spatio-temporal characteristics of the behavioral trend, the method distinguishes active valuable nodes from inactive ones and reveals typical evolutionary processes. We perform experiments on two practical online social networks with thousands of nodes. The experimental results demonstrate that the method can identify the active valuable nodes for information diffusion in large-scale temporal online social networks accurately and efficiently. It would be useful for business applications.

scholarly journals A Comparison of Online Social Networks and Real-Life Social Networks: A Study of Sina Microblogging

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Dayong Zhang ◽  
Guang Guo
Keyword(s):  
Social Networks ◽  
Structural Properties ◽  
Interpersonal Relationships ◽  
Online Social Networks ◽  
Social Life ◽  
Low Cost ◽  
Online Social Network ◽  
Real Life ◽  
Human Communication ◽  
Cognitive Constraints

Online social networks appear to enrich our social life, which raises the question whether they remove cognitive constraints on human communication and improve human social capabilities. In this paper, we analyze the users' following and followed relationships based on the data of Sina Microblogging and reveal several structural properties of Sina Microblogging. Compared with real-life social networks, our results confirm some similar features. However, Sina Microblogging also shows its own specialties, such as hierarchical structure and degree disassortativity, which all mark a deviation from real-life social networks. The low cost of the online network forms a broader perspective, and the one-way link relationships make it easy to spread information, but the online social network does not make too much difference in the creation of strong interpersonal relationships. Finally, we describe the mechanisms for the formation of these characteristics and discuss the implications of these structural properties for the real-life social networks.

scholarly journals Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social Networks

2021 ◽  
Vol 5 (1) ◽  
pp. 14
Author(s):  
Christos Makris ◽  
Georgios Pispirigos
Keyword(s):  
Social Networks ◽  
Graph Partitioning ◽  
Large Scale ◽  
Real Life ◽  
Information Networks ◽  
Digital Marketing ◽  
Partitioning Problems ◽  
Iterative Solutions ◽  
Community Extraction ◽  
Stability And Accuracy

Nowadays, due to the extensive use of information networks in a broad range of fields, e.g., bio-informatics, sociology, digital marketing, computer science, etc., graph theory applications have attracted significant scientific interest. Due to its apparent abstraction, community detection has become one of the most thoroughly studied graph partitioning problems. However, the existing algorithms principally propose iterative solutions of high polynomial order that repetitively require exhaustive analysis. These methods can undoubtedly be considered resource-wise overdemanding, unscalable, and inapplicable in big data graphs, such as today’s social networks. In this article, a novel, near-linear, and highly scalable community prediction methodology is introduced. Specifically, using a distributed, stacking-based model, which is built on plain network topology characteristics of bootstrap sampled subgraphs, the underlined community hierarchy of any given social network is efficiently extracted in spite of its size and density. The effectiveness of the proposed methodology has diligently been examined on numerous real-life social networks and proven superior to various similar approaches in terms of performance, stability, and accuracy.

scholarly journals Hashkat: large-scale simulations of online social networks

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Kevin Ryczko ◽  
Adam Domurad ◽  
Nicholas Buhagiar ◽  
Isaac Tamblyn
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Large Scale ◽  
Large Scale Simulations

scholarly journals Novel Node Centrality-Based Efficient Empirical Robustness Assessment for Directed Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xiaolong Deng ◽  
Hao Ding ◽  
Yong Chen ◽  
Cai Chen ◽  
Tiejun Lv
Keyword(s):  
Social Network ◽  
Vulnerability Assessment ◽  
Online Social Networks ◽  
Information Transfer ◽  
Large Scale ◽  
Real Life ◽  
Clustering Coefficient ◽  
Network Robustness ◽  
Directed Network ◽  
Online Social Media

In recent years, while extensive researches on various networks properties have been proposed and accomplished, little has been proposed and done on network robustness and node vulnerability assessment under cascades in directed large-scale online community networks. In essential, an online directed social network is a group-centered and information spread-dominated online platform which is very different from the traditional undirected social network. Some further research studies have indicated that the online social network has high robustness to random removals of nodes but fails to the intentional attacks, particularly to those attacks based on node betweenness or node directed coefficient. To explore on the robustness of directed social network, in this article, we have proposed two novel node centralities of ITG (information transfer gain-based probability clustering coefficient) and I M p v (directed path-based node importance centrality). These two new centrality models are designed to capture this cascading effect in directed online social networks. Furthermore, we also propose a new and highly efficient computing method based on iterations for I M p v . Then, with the abundant experiments on the synthetic signed network and real-life networks derived from directed online social media and directed human mobile phone calling network, it has been proved that our ITG and I M p v based on directed social network robustness and node vulnerability assessment method is more accurate, efficient, and faster than several traditional centrality methods such as degree and betweenness. And we also have proposed the solid reasoning and proof process of iteration times k in computation of I M p v . To the best knowledge of us, our research has drawn some new light on the leading edge of robustness on the directed social network.

Learning to Correlate Accounts Across Online Social Networks: An Embedding-Based Approach

2020 ◽  
Vol 32 (3) ◽  
pp. 714-729
Author(s):  
Fan Zhou ◽  
Kunpeng Zhang ◽  
Shuying Xie ◽  
Xucheng Luo
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Dimensional Space ◽  
Synthetic Data ◽  
Language Models ◽  
Data Sets ◽  
User Behaviors ◽  
Latent Representations ◽  
Multiple Accounts ◽  
Cross Site

Cross-site account correlation correlates users who have multiple accounts but the same identity across online social networks (OSNs). Being able to identify cross-site users is important for a variety of applications in social networks, security, and electronic commerce, such as social link prediction and cross-domain recommendation. Because of either heterogeneous characteristics of platforms or some unobserved but intrinsic individual factors, the same individuals are likely to behave differently across OSNs, which accordingly causes many challenges for correlating accounts. Traditionally, account correlation is measured by analyzing user-generated content, such as writing style, rules of naming user accounts, or some existing metadata (e.g., account profile, account historical activities). Accounts can be correlated by de-anonymizing user behaviors, which is sometimes infeasible since such data are not often available. In this work, we propose a method, called ACCount eMbedding (ACCM), to go beyond text data and leverage semantics of network structures, a possibility that has not been well explored so far. ACCM aims to correlate accounts with high accuracy by exploiting the semantic information among accounts through random walks. It models and understands latent representations of accounts using an embedding framework similar to sequences of words in natural language models. It also learns a transformation matrix to project node representations into a common dimensional space for comparison. With evaluations on both real-world and synthetic data sets, we empirically demonstrate that ACCM provides performance improvement compared with several state-of-the-art baselines in correlating user accounts between OSNs.

scholarly journals A Novel Wireless Sensor Network Evolution Model Based on Energy-Efficiency

2017 ◽  
Vol 13 (03) ◽  
pp. 4 ◽  
Author(s):  
Hui Gao ◽  
Zhixian Yang
Keyword(s):  
Preferential Attachment ◽  
Influence Factors ◽  
Network Evolution ◽  
Evolution Model ◽  
Wireless Sensor ◽  
Font Size ◽  
Total Energy Consumption ◽  
Scale Free ◽  
Random Node ◽  
Attachment Mechanism

<span style="font-family: 'Times New Roman',serif; font-size: 12pt; mso-fareast-font-family: SimSun; mso-fareast-theme-font: minor-fareast; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;">The Barabási–Albert (BA) model is a famous complex network model that generates scale-free networks. Wireless sensor networks (WSNs) had been thought to be approximately scale-free through lots of empirical research. Based on the BA model, we propose an evolution model for WSNs. According to actual influence factors such as the remainder energy of each sensor and physical link capability of each sensor, our evolution model constructs WSNs by using a preferential attachment mechanism. Through simulation and analysis, we can prove that our evolution model would make the total energy consumption of the WSNs more efficient and have a superior random node error tolerance.</span>

Prediction of Social Influence for Provenance of Misinformation in Online Social Network Using Big Data Approach

2020 ◽  
Author(s):  
Kumaran P ◽  
Rajeswari Sridhar
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Law Enforcement ◽  
Online Social Networks ◽  
Large Scale ◽  
Online Social Network ◽  
Root Cause ◽  
Minimum Number ◽  
Targeted Marketing ◽  
Implicit And Explicit

Abstract Online social networks (OSNs) is a platform that plays an essential role in identifying misinformation like false rumors, insults, pranks, hoaxes, spear phishing and computational propaganda in a better way. Detection of misinformation finds its applications in areas such as law enforcement to pinpoint culprits who spread rumors to harm the society, targeted marketing in e-commerce to identify the user who originates dissatisfaction messages about products or services that harm an organizations reputation. The process of identifying and detecting misinformation is very crucial in complex social networks. As misinformation in social network is identified by designing and placing the monitors, computing the minimum number of monitors for detecting misinformation is a very trivial work in the complex social network. The proposed approach determines the top suspected sources of misinformation using a tweet polarity-based ranking system in tandem with sarcasm detection (both implicit and explicit sarcasm) with optimization approaches on large-scale incomplete network. The algorithm subsequently uses this determined feature to place the minimum set of monitors in the network for detecting misinformation. The proposed work focuses on the timely detection of misinformation by limiting the distance between the suspected sources and the monitors. The proposed work also determines the root cause of misinformation (provenance) by using a combination of network-based and content-based approaches. The proposed work is compared with the state-of-art work and has observed that the proposed algorithm produces better results than existing methods.

scholarly journals Evolution Model of Spatial Interaction Network in Online Social Networking Services

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 434
Author(s):  
Jian Dong ◽  
Bin Chen ◽  
Pengfei Zhang ◽  
Chuan Ai ◽  
Fang Zhang ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Information Dissemination ◽  
Preferential Attachment ◽  
Spatial Interaction ◽  
Interaction Network ◽  
Evolution Model ◽  
Social Networking Services ◽  
The City ◽  
Real City

The development of online social networking services provides a rich source of data of social networks including geospatial information. More and more research has shown that geographical space is an important factor in the interactions of users in social networks. In this paper, we construct the spatial interaction network from the city level, which is called the city interaction network, and study the evolution mechanism of the city interaction network formed in the process of information dissemination in social networks. A network evolution model for interactions among cities is established. The evolution model consists of two core processes: the edge arrival and the preferential attachment of the edge. The edge arrival model arranges the arrival time of each edge; the model of preferential attachment of the edge determines the source node and the target node of each arriving edge. Six preferential attachment models (Random-Random, Random-Degree, Degree-Random, Geographical distance, Degree-Degree, Degree-Degree-Geographical distance) are built, and the maximum likelihood approach is used to do the comparison. We find that the degree of the node and the geographic distance of the edge are the key factors affecting the evolution of the city interaction network. Finally, the evolution experiments using the optimal model DDG are conducted, and the experiment results are compared with the real city interaction network extracted from the information dissemination data of the WeChat web page. The results indicate that the model can not only capture the attributes of the real city interaction network, but also reflect the actual characteristics of the interactions among cities.

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