scholarly journals Exploiting Temporal and Spatial Regularities for Content Dissemination in Opportunistic Social Network

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Zhiyuan Li ◽  
Junlei Bi ◽  
Carlos Borrego
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Success Rate ◽  
State Of The Art ◽  
Time Varying ◽  
Hop Count ◽  
Content Dissemination ◽  
System Overhead ◽  
Temporal And Spatial ◽  
Query Latency

Recently, content dissemination has become more and more important for opportunistic social networks. The challenges of opportunistic content dissemination result from random movement of nodes and uncertain positions of a destination, which seriously affect the efficiency of content dissemination. In this paper, we firstly construct time-varying interest communities based on the temporal and spatial regularities of users. Next, we design a content dissemination algorithm on the basis of time-varying interest communities. Our proposed content dissemination algorithm can run in O(nlog⁡n) time. Finally, the comparisons between the proposed content dissemination algorithm and state-of-the-art content dissemination algorithms show that our proposed content dissemination algorithm can (a) keep high query success rate, (b) reduce the average query latency, (c) reduce the hop count of a query, and (d) maintain low system overhead.

Incorporating Trust Relation with PMF to Enhance Social Network Recommendation Performance

2016 ◽  
Vol 30 (06) ◽  
pp. 1659016 ◽  
Author(s):  
Hui Li ◽  
Xiao-Ping Ma ◽  
Jun Shi
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Matrix Factorization ◽  
Web Applications ◽  
State Of The Art ◽  
Large Datasets ◽  
Trust Relation ◽  
The Empirical Analysis

In view of the exponential growth of information generated by social networks, social network analysis and recommendation have become important for many web applications. This paper examines the problem of social collaborative filtering to recommend items of interest to users in a social network setting. Many social networks capture the relationships among the nodes by using trust scores to label the edges. The bias of a node denotes its propensity to trust/mistrust its neighbors and is closely related to truthfulness. It is based on the idea that the recommendation of a highly biased node should be removed. In this paper, we propose a model-based approach for recommendation employing matrix factorization after removing the bias nodes from each link, which naturally fuses the users’ tastes and their trusted friends’ favors together. The empirical analysis on real large datasets demonstrate that our approaches outperform other state-of-the-art methods.

scholarly journals A P2P Query Algorithm for Opportunistic Networks Utilizing betweenness Centrality Forwarding

2013 ◽  
Vol 9 (4) ◽  
pp. 331-345
Author(s):  
Jianwei Niu ◽  
Mingzhu Liu ◽  
Han-Chieh Chao
Keyword(s):  
Social Network ◽  
Success Rate ◽  
Mobile Devices ◽  
Betweenness Centrality ◽  
State Of The Art ◽  
Opportunistic Networks ◽  
The State ◽  
Opportunistic Networking ◽  
Wireless Interfaces ◽  
Query Algorithm

With the proliferation of high-end mobile devices that feature wireless interfaces, many promising applications are enabled in opportunistic networks. In contrary to traditional networks, opportunistic networks utilize the mobility of nodes to relay messages in a store-carry-forward paradigm. Thus, the relay process in opportunistic networks faces several practical challenges in terms of delay and delivery rate. In this paper, we propose a novel P2P Query algorithm, namely Betweenness Centrality Forwarding (PQBCF), for opportunistic networking. PQBCF adopts a forwarding metric called Betweenness Centrality (BC), which is borrowed from social network, to quantify the active degree of nodes in the networks. In PQBCF, nodes with a higher BC are preferable to serve as relays, leading to higher query success rate and lower query delay. A comparison with the state-of-the-art algorithms reveals that PQBCF can provide better performance on both the query success Ratio and query delay, and approaches the performance of Epidemic Routing (ER) with much less resource consumption.

scholarly journals Co-authorship prediction in academic social network

2019 ◽  
Author(s):  
William Takahiro Maruyama ◽  
Luciano Antonio Digiampietri
Keyword(s):  
Artificial Intelligence ◽  
Social Networks ◽  
Social Network ◽  
State Of The Art ◽  
The State ◽  
Artificial Intelligence Techniques ◽  
Academic Social Network ◽  
Academic Social Networks

The prediction of relationships in a social network is a complex and extremely useful task to enhance or maximize collaborations by indicating the most promising partnerships. In academic social networks, prediction of relationships is typically used to try to identify potential partners in the development of a project and/or co-authors for publishing papers. This paper presents an approach to predict coauthorships combining artificial intelligence techniques with the state-of-the-art metrics for link predicting in social networks.

scholarly journals A Social Interaction Activity based Time-Varying User Vectorization Method for Online Social Networks

2018 ◽  
Author(s):  
Tianyi Hao ◽  
Longbo Huang
Keyword(s):  
Social Networks ◽  
Social Interaction ◽  
Online Social Networks ◽  
User Modeling ◽  
State Of The Art ◽  
Time Varying ◽  
Self Similarity ◽  
Pairwise Interactions ◽  
User Data ◽  
Model Training

In this paper, we consider the problem of user modeling in online social networks, and propose a social interaction activity based user vectorization framework, called the time-varying user vectorization (Tuv), to infer and make use of important user features. Tuv is designed based on a novel combination of word2vec, negative sampling and a smoothing technique for model training. It jointly handles multi-format user data and computes user representing vectors, by taking into consideration user feature variation, self-similarity and pairwise interactions among users. The framework enables us to extract hidden user properties and to produce user vectors. We conduct extensive experiments based on a real-world dataset, which show that Tuv significantly outperforms several state-of-the-art user vectorization methods.

scholarly journals Conducting social network analysis with animal telemetry data: applications and methods using spatsoc

2018 ◽  
Author(s):  
Alec L. Robitaille ◽  
Quinn M.R. Webber ◽  
Eric Vander Wal
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
R Package ◽  
Large Animal ◽  
List Type ◽  
Telemetry Data ◽  
Group Data ◽  
Temporal And Spatial

SummaryWe present spatsoc: an R package for conducting social network analysis with animal telemetry data.Animal social network analysis is a method for measuring relationships between individuals to describe social structure. Using animal telemetry data for social network analysis requires functions to generate proximity-based social networks that have flexible temporal and spatial grouping. Data can be complex and relocation frequency can vary so the ability to provide specific temporal and spatial thresholds based on the characteristics of the species and system is required.spatsoc fills a gap in R packages by providing flexible functions, explicitly for animal telemetry data, to generate gambit-of-the-group data, perform data-stream randomization and generate group by individual matrices.The implications of spatsoc are that current users of large animal telemetry or otherwise georeferenced data for movement or spatial analyses will have access to efficient and intuitive functions to generate social networks.

scholarly journals Rumour prevention in social networks with layer 2 blockchains

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Subhasis Thakur ◽  
John G. Breslin
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
State Of The Art ◽  
Privacy Preserving ◽  
The State ◽  
Media Content ◽  
The Social ◽  
Layer 2 ◽  
Art Methods

AbstractSocial bots can cause social, political, and economical disruptions by spreading rumours. The state-of-the-art methods to prevent social bots from spreading rumours are centralised and such solutions may not be accepted by users who may not trust a centralised solution being biased. In this paper, we developed a decentralised method to prevent social bots. In this solution, the users of a social network create a secure and privacy-preserving decentralised social network and may accept social media content if it is sent by its neighbour in the decentralised social network. As users only choose their trustworthy neighbours from the social network to be part of its neighbourhood in the decentralised social network, it prevents the social bots to influence a user to accept and share a rumour. We prove that the proposed solution can significantly reduce the number of users who are share rumour.

scholarly journals Cross-Modal Search for Social Networks via Adversarial Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Nan Zhou ◽  
Junping Du ◽  
Zhe Xue ◽  
Chong Liu ◽  
Jinxuan Li
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Search ◽  
State Of The Art ◽  
Visual Features ◽  
Adversarial Learning ◽  
Sina Weibo ◽  
Modal Data ◽  
Social Network Data ◽  
Correlation Learning

Cross-modal search has become a research hotspot in the recent years. In contrast to traditional cross-modal search, social network cross-modal information search is restricted by data quality for arbitrary text and low-resolution visual features. In addition, the semantic sparseness of cross-modal data from social networks results in the text and visual modalities misleading each other. In this paper, we propose a cross-modal search method for social network data that capitalizes on adversarial learning (cross-modal search with adversarial learning: CMSAL). We adopt self-attention-based neural networks to generate modality-oriented representations for further intermodal correlation learning. A search module is implemented based on adversarial learning, through which the discriminator is designed to measure the distribution of generated features from intramodal and intramodal perspectives. Experiments on real-word datasets from Sina Weibo and Wikipedia, which have similar properties to social networks, show that the proposed method outperforms the state-of-the-art cross-modal search methods.

scholarly journals Vulnerabilities to Online Social Network Identity Deception Detection Research and Recommendations for Mitigation

2020 ◽  
Vol 12 (9) ◽  
pp. 148 ◽  
Author(s):  
Max Ismailov ◽  
Michail Tsikerdekis ◽  
Sherali Zeadally
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Real World ◽  
Online Social Networks ◽  
Laboratory Experiments ◽  
Deception Detection ◽  
State Of The Art ◽  
Online Social Network ◽  
Ongoing Research ◽  
The Real

Identity deception in online social networks is a pervasive problem. Ongoing research is developing methods for identity deception detection. However, the real-world efficacy of these methods is currently unknown because they have been evaluated largely through laboratory experiments. We present a review of representative state-of-the-art results on identity deception detection. Based on this analysis, we identify common methodological weaknesses for these approaches, and we propose recommendations that can increase their effectiveness for when they are applied in real-world environments.

The Fission of the Forbidden: The Popularity of Video Content in an Online Right-Wing Extremist Environment

2021 ◽  
Vol 5 (1) ◽  
pp. 51-67
Author(s):  
Lise Waldek
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Connectedness ◽  
Right Wing ◽  
Video Content ◽  
Content Dissemination ◽  
Video Sharing ◽  
High Engagement

Abstract Video platforms such as YouTube provide an environment where the blurred duality between content dissemination and creation facilitates the generation of social networks. Research into online violent extremist environments has often noted the prominence of video-sharing platforms as a means of distributing propaganda and cultivating social networks for purposes of recruitment. This paper draws from the study of emotion to examine three videos and associated comments that had high engagement, understood as the frequency of interactions, likes/upvotes and reposts in a given social network, in a right-wing extremist online milieu. This analysis highlights the important role emotions play in generating social connectedness and ultimately engagement and recruitment into online right-wing extremist milieus. Understanding the significance of emotions in online violent extremist video content can help to identify opportunities for moderation and/or the construction of alternative narratives.

An Approach to Extract New Keywords From Radical Groups in Social Networks

2021 ◽  
Vol 11 (1) ◽  
pp. 54-74
Author(s):  
Geetika Sarna ◽  
M. P. S. Bhatia
Keyword(s):  
Social Networks ◽  
Social Network ◽  
State Of The Art ◽  
Probabilistic Approach ◽  
Significant Part ◽  
The Social ◽  
Terrorist Groups ◽  
Short Span ◽  
Hidden Patterns

In recent times, numerous users as well as communities on social networks post messages in multimedia formats. The significant part of the message is the keyword that would help in recognizing the theme of information. Hence, this research aims to determine the new keywords occur in the messages posted on social network which would also be beneficial in identifying the category of user, various communities, and hidden patterns exist in the social network. In this paper, probabilistic approach is applied to identify the new keywords from the radical groups. Radical groups are those whose demeanor is totally opposite to the acceptance of community, for instance, terrorist groups. Hence, the dataset of terrorist community extracted from Twitter is used to find the new keywords that occur for a short span of time. State-of-the-art studies carried out the identification of terrorist communities based on keywords already present in lexicon, but the proposed approach makes the decision on the basis of both old as well as new keywords.

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