scholarly journals Research on Critical Nodes Algorithm in Social Complex Networks

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 68-73
Author(s):  
Xue-Guang Wang
Keyword(s):  
Social Networks ◽  
Monte Carlo ◽  
Community Structure ◽  
Social Network ◽  
Monte Carlo Method ◽  
Information Diffusion ◽  
Marginal Contribution ◽  
Critical Nodes ◽  
Marginal Contributions ◽  
Synthetic Datasets

AbstractDiscovering critical nodes in social networks has many important applications and has attracted more and more institutions and scholars. How to determine the K critical nodes with the most influence in a social network is a NP (define) problem. Considering the widespread community structure, this paper presents an algorithm for discovering critical nodes based on two information diffusion models and obtains each node’s marginal contribution by using a Monte-Carlo method in social networks. The solution of the critical nodes problem is the K nodes with the highest marginal contributions. The feasibility and effectiveness of our method have been verified on two synthetic datasets and four real datasets.

scholarly journals An Algorithm for Critical Nodes Problem in Social Networks Based on Owen Value

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Xue-Guang Wang
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Marginal Contribution ◽  
Actual Situation ◽  
Owen Value ◽  
Critical Node ◽  
Critical Nodes ◽  
Synthetic Datasets ◽  
Critical Node Problem ◽  
Node Problem

Discovering critical nodes in social networks has many important applications. For finding out the critical nodes and considering the widespread community structure in social networks, we obtain each node’s marginal contribution by Owen value. And then we can give a method for the solution of the critical node problem. We validate the feasibility and effectiveness of our method on two synthetic datasets and six real datasets. At the same time, the result obtained by using our method to analyze the terrorist network is in line with the actual situation.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

Classifying the Influential Individuals in Multi-Layer Social Networks

2019 ◽  
Vol 8 (1) ◽  
pp. 21-32
Author(s):  
Ruchi Mittal ◽  
M.P.S Bhatia
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Diffusion ◽  
Real Life ◽  
Single Layer ◽  
Machine Learning Techniques ◽  
Centrality Measures ◽  
Learning Techniques ◽  
Modes Of Interaction ◽  
Source Of Information

Nowadays, social media is one of the popular modes of interaction and information diffusion. It is commonly found that the main source of information diffusion is done by some entities and such entities are also called as influencers. An influencer is an entity or individual who has the ability to influence others because of his/her relationship or connection with his/her audience. In this article, we propose a methodology to classify influencers from multi-layer social networks. A multi-layer social network is the same as a single layer social network depict that it includes multiple properties of a node and modeled them into multiple layers. The proposed methodology is a fusion of machine learning techniques (SVM, neural networks and so on) with centrality measures. We demonstrate the proposed algorithm on some real-life networks to validate the effectiveness of the approach in multi-layer systems.

scholarly journals Dynamical Modeling, Analysis, and Control of Information Diffusion over Social Networks: A Deep Learning-Based Recommendation Algorithm in Social Network

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Kefei Cheng ◽  
Xiaoyong Guo ◽  
Xiaotong Cui ◽  
Fengchi Shan
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Social Network ◽  
Information Diffusion ◽  
Recommendation Algorithm ◽  
Central Sequence ◽  
Modeling Analysis ◽  
News Recommendation ◽  
Browsing Behavior ◽  
And Control

The recommendation algorithm can break the restriction of the topological structure of social networks, enhance the communication power of information (positive or negative) on social networks, and guide the information transmission way of the news in social networks to a certain extent. In order to solve the problem of data sparsity in news recommendation for social networks, this paper proposes a deep learning-based recommendation algorithm in social network (DLRASN). First, the algorithm is used to process behavioral data in a serializable way when users in the same social network browse information. Then, global variables are introduced to optimize the encoding way of the central sequence of Skip-gram, in which way online users’ browsing behavior habits can be learned. Finally, the information that the target users’ have interests in can be calculated by the similarity formula and the information is recommended in social networks. Experimental results show that the proposed algorithm can improve the recommendation accuracy.

Centrality measure based approach for detection of malicious nodes in twitter social network

2018 ◽  
Vol 7 (4.5) ◽  
pp. 518 ◽  
Author(s):  
Krishna Das ◽  
Smriti Kumar Sinha
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Diffusion ◽  
Opinion Mining ◽  
Theoretical Method ◽  
Short Paper ◽  
Malicious Nodes ◽  
Data Set ◽  
Centrality Measure ◽  
The Social

In this short paper, network structural measure called centrality measure based mathematical approach is used for detection of malicious nodes in twitter social network. One of the objectives in analysing social networks is to detect malicious nodes which show anomaly behaviours in social networks. There are different approaches for anomaly detection in social networks such as opinion mining methods, behavioural methods, network structural approach etc. Centrality measure, a graph theoretical method related to social network structure, can be used to categorize a node either as popular and influential or as non-influential and anomalous node. Using this approach, we have analyzed twitter social network to remove anomalous nodes from the nodes-edges twitter data set. Thus removal of these kinds of nodes which are not important for information diffusion in the social network, makes the social network clean & speedy in fast information propagation.   

Network Redundancy and Information Diffusion: The Impacts of Information Redundancy, Similarity, and Tie Strength

2016 ◽  
Vol 46 (2) ◽  
pp. 250-272 ◽  
Author(s):  
Hai Liang ◽  
King-wa Fu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information Diffusion ◽  
Tie Strength ◽  
Information Redundancy ◽  
Ego Networks ◽  
Core Concepts ◽  
The Impact

It remains controversial whether community structures in social networks are beneficial or not for information diffusion. This study examined the relationships among four core concepts in social network analysis—network redundancy, information redundancy, ego-alter similarity, and tie strength—and their impacts on information diffusion. By using more than 6,500 representative ego networks containing nearly 1 million following relationships from Twitter, the current study found that (1) network redundancy is positively associated with the probability of being retweeted even when competing variables are controlled for; (2) network redundancy is positively associated with information redundancy, which in turn decreases the probability of being retweeted; and (3) the inclusion of both ego-alter similarity and tie strength can attenuate the impact of network redundancy on the probability of being retweeted.

scholarly journals Towards a Social Cloud Framework for Collaborative eResearch

2021 ◽  
Author(s):  
◽  
Ashfag M. Thaufeeg
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Paradigm Shift ◽  
Research Community ◽  
Online Research ◽  
Community Groups ◽  
The Social ◽  
Cloud Framework ◽  
Social Cloud ◽  
Synthetic Datasets

<p>Collaboration has always been an important aspect of scientific research. The coming of internet opened the doors for greater levels of collaboration for the research community, first enabled by email and then by web 2.0 based online portals called VREs. A new force, social networks, are bringing a paradigm shift to online research communities. Social networks could foster a more vibrant research environment powered by social activities such as sharing, community creation, tagging and community groups. This thesis explores the idea of using the power of social networks to create a social cloud to contribute and share computing resources. The prototype implementation, called the Social Collaborative Cloud (SoCC), uses facebook as the underlying social network. The prototype was evaluated using simulations of both real and synthetic datasets, as well as real world tests.</p>

Crowd attraction-driven community evolution on social network

2021 ◽  
pp. 2250009
Author(s):  
Fuzhong Nian ◽  
Li Luo ◽  
Xuelong Yu
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Social Network ◽  
Large Scale ◽  
Detection Algorithm ◽  
Information Propagation ◽  
Evolution Analysis ◽  
Dynamic Social Networks ◽  
Community Evolution ◽  
Community Detection Algorithm

The evolution analysis of community structure of social network will help us understand the composition of social organizations and the evolution of society better. In order to discover the community structure and the regularity of community evolution in large-scale social networks, this paper analyzes the formation process and influencing factors of communities, and proposes a community evolution analysis method of crowd attraction driven. This method uses the traditional community division method to divide the basic community, and introduces the theory of information propagation into complex network to simulate the information propagation of dynamic social networks. Then defines seed node, the activity of basic community and crowd attraction to research the influence of groups on individuals in social networks. Finally, making basic communities as fixed groups in the network and proposing community detection algorithm based on crowd attraction. Experimental results show that the scheme can effectively detect and identify the community structure in large-scale social networks.

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