Constructed Link Prediction Model by Relation Pattern on the Social Network

With the rapid development of the Internet, social networks have shown an unprecedented development trend among college students. Closer social activities among college students have led to the emergence of college students with new social characteristics. The traditional method of college students’ group classification can no longer meet the current demand. Therefore, this paper proposes a social network link prediction method-combination algorithm, which combines neighbor information and a random block. By mining the social networks of college students’ group relationships, the classification of college students’ groups can be realized. Firstly, on the basis of complex network theory, the essential relationship of college student groups under a complex network is analyzed. Secondly, a new combination algorithm is proposed by using the simplest linear combination method to combine the proximity link prediction based on neighbor information and the likelihood analysis link prediction based on a random block. Finally, the proposed combination algorithm is verified by using the social data of college students’ networks. Experimental results show that, compared with the traditional link prediction algorithm, the proposed combination algorithm can effectively dig out the group characteristics of social networks and improve the accuracy of college students’ association classification.

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A Novel Method to Dynamically Fix Threshold for Node Neighbourhood Based Link Prediction Techniques

International Journal of Information Technology and Web Engineering ◽

10.4018/ijitwe.2020010102 ◽

2020 ◽

Vol 15 (1) ◽

pp. 17-34

Author(s):

Anand Kumar Gupta ◽

Neetu Sardana

Keyword(s):

Social Network ◽

Link Prediction ◽

Binary Classification ◽

Online Social Network ◽

Threshold Value ◽

Classification Problem ◽

Similarity Score ◽

The Social ◽

Comparative Results ◽

Prediction Techniques

The objective of an online social network is to amplify the stream of information among the users. This goal can be accomplished by maximizing interconnectivity among users using link prediction techniques. Existing link prediction techniques uses varied heuristics such as similarity score to predict possible connections. Link prediction can be considered a binary classification problem where probable class outcomes are presence and absence of connections. One of the challenges in classification is to decide threshold value. Since the social network is exceptionally dynamic in nature and each user possess different features, it is difficult to choose a static, common threshold which decides whether two non-connected users will form interconnectivity. This article proposes a novel technique, FIXT, that dynamically decides the threshold value for predicting the possibility of new link formation. The article evaluates the performance of FIXT with six baseline techniques. The comparative results depict that FIXT achieves accuracy up to 93% and outperforms baseline techniques.

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Similarity-Based Indices or Metrics for Link Prediction

Advances in Social Networking and Online Communities - Hidden Link Prediction in Stochastic Social Networks ◽

10.4018/978-1-5225-9096-5.ch001 ◽

2019 ◽

pp. 1-29

Author(s):

Praveen Kumar Bhanodia ◽

Kamal Kumar Sethi ◽

Aditya Khamparia ◽

Babita Pandey ◽

Shaligram Prajapat

Keyword(s):

Social Networks ◽

Social Network ◽

Link Prediction ◽

Online Social Network ◽

Dynamic Networks ◽

Relevant Information ◽

Network Evolution ◽

Problem Instance ◽

The Social ◽

Social Network Evolution

Link prediction in social network has gained momentum with the inception of machine learning. The social networks are evolving into smart dynamic networks possessing various relevant information about the user. The relationship between users can be approximated by evaluation of similarity between the users. Online social network (OSN) refers to the formulation of association (relationship/links) between users known as nodes. Evolution of OSNs such as Facebook, Twitter, Hi-Fi, LinkedIn has provided a momentum to the growth of such social networks, whereby millions of users are joining it. The online social network evolution has motivated scientists and researchers to analyze the data and information of OSN in order to recommend the future friends. Link prediction is a problem instance of such recommendation systems. Link prediction is basically a phenomenon through which potential links between nodes are identified on a network over the period of time. In this chapter, the authors describe the similarity metrics that further would be instrumental in recognition of future links between nodes.

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A Community Bridge Boosting Social Network Link Prediction Model

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 - ASONAM '17 ◽

10.1145/3110025.3110143 ◽

2017 ◽

Cited By ~ 3

Author(s):

Fei Gao ◽

Katarzyna Musial ◽

Bogdan Gabrys

Keyword(s):

Social Network ◽

Prediction Model ◽

Link Prediction ◽

Network Link

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A Weight based Scheme for Improving the Accuracy of Relationship in Social Network

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k2327.0981119 ◽

2019 ◽

Vol 8 (11) ◽

pp. 3040-3043 ◽

Cited By ~ 3

Keyword(s):

Social Media ◽

Social Network ◽

Link Prediction ◽

Performance Metrics ◽

Prediction Algorithm ◽

Relationship Matrix ◽

Community Network ◽

The Social ◽

Relationship Value ◽

The Relationship

The usage of social media has become unavoidable in the last decade. The social media is highly dynamic in nature and grows rapidly. The community network offers a rich expedient of various data. The detection of communities is based on the frequency in the networks which is usually represented by graphs. The vertices (nodes) are representing the social actor and the edges (links) represent the relation between those actors. The community link detection is as hard as the graph increases up to millions of vertices and edges. The accuracy of link prediction for inferring missing (erased or broken) links is very complex due to the dynamic nature of links. The links are updated from time to time and the new links are established dynamically. As the links are appeared and disappeared dynamically, the accuracy of identifying the edges of the social network graph of the user is complex in nature. Many efforts have been put up in developing link prediction algorithms in the past, but still there is a lacuna in accuracy in predicting inferred / broken links. A weight based link prediction algorithm is proposed to improve the accuracy of the link prediction on inferred / broken links in the social media. In this method, a weight based link analysis is employed to quantify the relative value between two nodes in the community network. The correlation value for relationship is also determined over a period of time using the designed relationship matrix. The relationship value between the nodes is computed by a Euclidian distance approach. The relationship value of each node is determined by the relationship equation using weight values. The proposed approach is experimented in constrained environment for 2 users’ Facebook usages over a period of a year. The accuracy of relationship is used as performance metrics. The results shown that the accuracy is improved 2.35% more than random predictor method

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Hybrid Neuro-Fuzzy Classification Algorithm for Social Network

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f8537.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 2434-2437

Keyword(s):

Social Network ◽

Link Prediction ◽

Training Data ◽

Fuzzy Classification ◽

Membership Functions ◽

Output Surface ◽

Neuro Fuzzy ◽

The Social ◽

Testing Error ◽

Generation Rule

People tend to build and maintain their friendship relying on SNS nowadays. Thus, the problem of how to organize the social network accurately and automatically. In this paper, a hybrid neuro-fuzzy approach is used. . Many aspect impact the error values like input/output, membership functions, the training data arrays, and the number of epochs needed to train the model. This paper is based on hybrid Neuro-Fuzzy concept for testing the link prediction for facebook data. We use Matlab to calculate average testing Error, View Generation Rule, Output Surface.

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Missing Link Prediction Using Non-Overlapped Features and Multiple Sources of Social Networks

Information ◽

10.3390/info12050214 ◽

2021 ◽

Vol 12 (5) ◽

pp. 214

Author(s):

Pokpong Songmuang ◽

Chainarong Sirisup ◽

Aroonwan Suebsriwichai

Keyword(s):

Machine Learning ◽

Social Networks ◽

Social Network ◽

Prediction Model ◽

Link Prediction ◽

Classification Model ◽

Density Level ◽

Missing Link ◽

Common Features ◽

The Common

The current methods for missing link prediction in social networks focus on using data from overlapping users from two social network sources to recommend links between unconnected users. To improve prediction of the missing link, this paper presents the use of information from non-overlapping users as additional features in training a prediction model using a machine-learning approach. The proposed features are designed to use together with the common features as extra features to help in tuning up for a better classification model. The social network data sources used in this paper are Twitter and Facebook where Twitter is a main data for prediction and Facebook is a supporting data. For evaluations, a comparison using different machine-learning techniques, feature settings, and different network-density level of data source is studied. The experimental results can be concluded that the prediction model using a combination of the proposed features and the common features with Random Forest technique gained the best efficiency using percentage amount of recovering missing links and F1 score. The model of combined features yields higher percentage of recovering link by an average of 23.25% and the F1-measure by an average of 19.80% than the baseline of multi-social network source.

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Predicting investor funding behavior using crunchbase social network features

Internet Research ◽

10.1108/intr-09-2014-0231 ◽

2016 ◽

Vol 26 (1) ◽

pp. 74-100 ◽

Cited By ~ 12

Author(s):

Yuxian Eugene Liang ◽

Soe-Tsyr Daphne Yuan

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Link Prediction ◽

Social Relationship ◽

Jaccard Coefficient ◽

Network Graph ◽

Content Type ◽

The Social ◽

Relationship Factors

Purpose – What makes investors tick? Largely counter-intuitive compared to the findings of most past research, this study explores the possibility that funding investors invest in companies based on social relationships, which could be positive or negative, similar or dissimilar. The purpose of this paper is to build a social network graph using data from CrunchBase, the largest public database with profiles about companies. The authors combine social network analysis with the study of investing behavior in order to explore how similarity between investors and companies affects investing behavior through social network analysis. Design/methodology/approach – This study crawls and analyzes data from CrunchBase and builds a social network graph which includes people, companies, social links and funding investment links. The problem is then formalized as a link (or relationship) prediction task in a social network to model and predict (across various machine learning methods and evaluation metrics) whether an investor will create a link to a company in the social network. Various link prediction techniques such as common neighbors, shortest path, Jaccard Coefficient and others are integrated to provide a holistic view of a social network and provide useful insights as to how a pair of nodes may be related (i.e., whether the investor will invest in the particular company at a time) within the social network. Findings – This study finds that funding investors are more likely to invest in a particular company if they have a stronger social relationship in terms of closeness, be it direct or indirect. At the same time, if investors and companies share too many common neighbors, investors are less likely to invest in such companies. Originality/value – The author’s study is among the first to use data from the largest public company profile database of CrunchBase as a social network for research purposes. The author ' s also identify certain social relationship factors that can help prescribe the investor funding behavior. Authors prediction strategy based on these factors and modeling it as a link prediction problem generally works well across the most prominent learning algorithms and perform well in terms of aggregate performance as well as individual industries. In other words, this study would like to encourage companies to focus on social relationship factors in addition to other factors when seeking external funding investments.

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