Link Prediction In Social Networks Research Articles

A novel particle swarm optimization-based intelligence link prediction algorithm in real world networks

Link prediction in social network is an important topic due to its applications like finding collaborations and recommending friends. Among existing link prediction methods, similarity-based approaches are found to be most effective since they examine the number of common neighbours (CN). Current work presents a novel link prediction algorithm based on particle swarm optimization (PSO) and implemented on four real world datasets namely, Zachary’s karate club (ZKC), bottlenose dolphin network (BDN), college football network (CFN) and Krebs’ books on American politics (KBAP). It consists of three experiments: i) to find the measures on existing methods and compare them with our proposed algorithm; ii) to find the measured values of the existing methods along with our proposed one to determine future links among nodes that have no CN; and iii) to find the measures of the methods to determine future links among nodes having same number of CN. In experiment 1, our proposed approach achieved 75.88%, 78.34%, 82.63% and 78.36% accuracy for ZKC, BDN, CFN, and KBAP respectively. These results beat the performances of traditional algorithms. In experiment 2, the accuracies are found as 75.53%, 74.25%, 81.63% and 78.34% respectively. In experiment 3, accuracies are detected as 72.75%, 81.53%, 78.35% and 75.13% respectively.

Link prediction in social networks using hyper-motif representation on hypergraph

Link prediction in social networks using hyper-motif representation on hypergraph

Node2Vec and Machine Learning: A Powerful Duo for Link Prediction in Social Network

Link prediction in social networks is a challenging task that attempts to uncover hidden linkages and forecast future connections. The link prediction problem is addressed in this research article by utilizing the capabilities of Node2Vec and machine learning algorithms. To learn high-dimensional node representations that capture both local and global network structures, the Node2Vec technique is used. Then, in order to forecast potential connections, these node embedding’s are put into various machine learning models. Two real-world social network datasets are used to test the suggested methodology, and the findings show a considerable improvement in link prediction accuracy. It achieves a deeper comprehension of the hidden relationships in social networks by fusing the semantic richness of Node2Vec embedding’s with the predictive powers of machine learning methods. The results of this study extend link prediction approaches in social networks by revealing hidden ties and providing insightful predictions for upcoming connections. The suggested method indicates the potential for real-world applications in a number of fields, including recommender systems, targeted advertising, and social influence studies.

A hybrid approach for enhanced link prediction in social networks based on community detection

Community detection and link prediction are interdependent to a high degree. Knowing the community structure beforehand improves the identification of missing links, whereas clustering on networks with newly introduced missing links improves community detection. In this work, we examine the effectiveness of employing community structure information to predict links in static networks by combining local, quasi-local, and global similarity features to compensate for the weaknesses of each approach. Moreover, we formally defined two classes of links, called relevant links, based on the network's community structure. These links are important because they connect communities or distant nodes within communities. To solve these issues, we developed two hybrid link prediction algorithms based on network communities. To evaluate the effectiveness of the proposed hybrid algorithms, we conducted a comprehensive computational campaign using both real-world and synthetic data-sets. Experiments show that adding information on communities and relevant links enhances the accuracy of link prediction.

Embedding-based Method for the Supervised Link Prediction in Social Networks

In recent years, social network analysis has received a lot of interest. Link prediction is an important area of research in this field that uses information from current networks to predict the likely links that will emerge in the future. It has attracted considerable attention from interdisciplinary research communities due to its ubiquitous applications in biological networks, computer science, transportation networks, bioinformatics, telecommunication networks, and so on. Currently, supervised machine learning is one of the critical techniques in the link prediction task. Several algorithms have been developed by many authors to predict the future link in the network, but there is still scope to improve the previous approaches. In the supervised link prediction process, feature selection is a crucial step. Most existing algorithms use one type of similarity-based feature to represent data, which is not well described due to the large scale and heterogeneity of social networks. One of the newest techniques for link prediction is embedding methods, which are used to preparing the feature vector for each the nonexisting links in the network. In this paper, we introduce a novel approach to supervised link prediction based on feature embedding methods in order to achieve better performance. Our contribution considers a set of embedding methods as the feature vector for training the machine learning classifiers. The main focus of this work is to investigate the relevance of different feature embedding methods to improve the performance of the supervised link prediction models. The experimental results on some real-world temporal networks revealed satisfactory results, which encourage us for further analysis. Moreover, the use of feature embedding methods will provide better performance in this regard.

Interpretable Signed Link Prediction With Signed Infomax Hyperbolic Graph

Signed link prediction in social networks aims to reveal the underlying relationships (i.e., links) among users (i.e., nodes) given their existing positive and negative interactions observed. Most of the prior efforts are devoted to learning node embeddings with graph neural networks (GNNs), which preserve the signed network topology by message-passing along edges to facilitate the downstream link prediction task. Nevertheless, the existing graph-based approaches could hardly provide human-intelligible explanations for the following three questions: (1) which neighbors to aggregate, (2) which path to propagate along, and (3) which social theory to follow in the learning process. To answer the aforementioned questions, in this paper, we investigate how to reconcile the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">balance</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">status</i> social rules with information theory and develop a unified framework, termed as Signed Infomax Hyperbolic Graph ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SIHG</b> ). By maximizing the mutual information between edge polarities and node embeddings, one can identify the most representative neighboring nodes that support the inference of edge sign. Different from existing GNNs that could only group features of friends in the subspace, the proposed SIHG incorporates the signed attention module, which is also capable of pushing hostile users far away from each other to preserve the geometry of antagonism. The polarity of the learned edge attention maps, in turn, provides interpretations of the social theories used in each aggregation. In order to model high-order user relations and complex hierarchies, the node embeddings are projected and measured in a hyperbolic space with a lower distortion. Extensive experiments on four signed network benchmarks demonstrate that the proposed SIHG framework significantly outperforms the state-of-the-arts in signed link prediction.

Evolutionary Features for Dynamic Link Prediction in Social Networks

One of the inherent characteristics of dynamic networks is the evolutionary nature of their constituents (i.e., actors and links). As a time-evolving model, the link prediction mechanism in dynamic networks can successfully capture the underlying growth mechanisms of social networks. Mining the temporal patterns of dynamic networks has led researchers to utilise dynamic information for dynamic link prediction. Despite several methodological improvements in dynamic link prediction, temporal variations of actor-level network structure and neighbourhood information have drawn little attention from the network science community. Evolutionary aspects of network positional changes and associated neighbourhoods, attributed to non-connected actor pairs, may suitably be used for predicting the possibility of their future associations. In this study, we attempted to build dynamic similarity metrics by considering temporal similarity and correlation between different actor-level evolutionary information of non-connected actor pairs. These metrics then worked as dynamic features in the supervised link prediction model, and performances were compared against static similarity metrics (e.g., AdamicAdar). Improved performance is achieved by the metrics considered in this study, representing them as prospective candidates for dynamic link prediction tasks and to help understand the underlying evolutionary mechanism.

Self-Configured Framework for scalable link prediction in twitter: Towards autonomous spark framework

Scalable link prediction in social networks allow dynamic social interaction gathering, potential friend suggestions, and community detection. Distributed open-source frameworks such as Hadoop and Spark facilitate efficient link prediction especially in large-scale social networks. The frameworks provide different kinds of tunable properties for users to manually configure the parameters for the applications. However, manual configurations are open to performance issues when the applications start scaling tremendously, which are hard to set up and are exposed to human errors. This paper proposes a novel Self-Configured Framework (SCF) to provide an autonomous feature in Spark that predicts and sets the best configuration instantly before the application execution using the XGBoost classifier. The framework with a self-configuration setting demonstrates a 40% reduction in prediction time as well as a balanced resource consumption that makes full use of resources, especially for limited number and size of clusters. The presented framework establishes its efficiency for link prediction in large-scale social networks by automatically configuring the best configuration suitable for a specific application given the varying dataset size of the Twitter social network, workload, and cluster specification.

ELP: Link prediction in social networks based on ego network perspective

Social network analysis has recently been of much interest to researchers in diverse fields. This increased attention is due to its broad applicability in modeling complex real-world scenarios (problems). Link prediction is a crucial issue in social network analysis, one that finds the likelihood of having a link between two nodes in the network. Of the existing methods, many use topological network properties, while others use algebraic methods, statistical models, node embeddings and, community information. Although some path-based approaches can be said to deal with some nodes’ commutative effect at some point, they are not designed to infer the total community effect of all local nodes on a specific link. Hence we present ELP, a link prediction method based on the Ego perspective. First, this approach computes each existing edge’s ego strength using ego networks, which can be construed as regions of influence of specific nodes. These ego strengths can be abstracted as the total effect of all local nodes on a particular edge. Then we utilize a topological feature set to estimate the prediction scores for target links. This feature set is selected after observing the performance of five different possible topological feature sets. Finally, we perform experiments on real-world networks to validate our algorithm’s performance and compare it with state-of-the-art algorithms. The statistical tests justify the significant difference of our proposed method from the state-of-the-art algorithms.

GDPR-Compliant Social Network Link Prediction in a Graph DBMS: The Case of Know-How Development at Beekeeper

The amount of available information in the digital world contains massive amounts of data, far more than people can consume. Beekeeper AG provides a GDPR-compliant platform for frontline employees, who typically do not have permanent access to digital information. Finding relevant information to perform their job requires efficient filtering principles to reduce the time spent on searching, thus saving work hours. However, with GDPR, it is not always possible to observe user identification and content. Therefore, this paper proposes link prediction in a graph structure as an alternative to presenting the information based on GDPR data. In this study, the research of user interaction data in a graph database was compared with graph machine learning algorithms for extracting and predicting network patterns among the users. The results showed that although the accuracy of the models was below expectations, the know-how developed during the process could generate valuable technical and business insights for Beekeeper AG.

Expanded graph embedding for joint network alignment and link prediction

Link prediction in social networks has been an active field of study in recent years fueled by the rapid growth of many social networks. Many link prediction methods are harmed by users’ intention of avoiding being traced across networks. They may provide inaccurate information or overlook a great deal of information in multiple networks. This problem was overcome by developing methods for predicting links in a network based on known links in another network. Node alignment between the two networks significantly improves the efficiency of those methods. This research proposes a new embedding method to improve link prediction and node alignment results. The proposed embedding method is based on the Expanded Graph, which is our new novel network that has edges from both networks in addition to edges across the networks. Matrix factorization on the Finite Step Transition and Laplacian similarity matrices of the Expanded Graph has been used to obtain the embeddings for the nodes. Using the proposed embedding techniques, we jointly run network alignment and link prediction tasks iteratively to let them optimize each other’s results. We performed extensive experiments on many datasets to examine the proposed method. We achieved significant improvements in link prediction precision, which was 50% better than the peer’s method, and in recall, which was 500% better in some datasets. We also scale down the processing time of the solution to be more applicable to big social networks. We conclude that computed embedding in this type of problem is more suitable than learning the embedding since it shortens the processing time and gives better results.

A novel similarity measure of link prediction in bipartite social networks based on neighborhood structure

AbstractLink prediction is one of the methods of social network analysis. Bipartite networks are a type of complex network that can be used to model many natural events. In this study, a novel similarity measure for link prediction in bipartite networks is presented. Due to the fact that classical social network link prediction methods are less efficient and effective for use in bipartite network, it is necessary to use bipartite network-specific methods to solve this problem. The purpose of this study is to provide a centralized and comprehensive method based on the neighborhood structure that performs better than the existing classical methods. The proposed method consists of a combination of criteria based on the neighborhood structure. Here, the classical criteria for link prediction by modifying the bipartite network are defined. These modified criteria constitute the main component of the proposed similarity measure. In addition to low simplicity and complexity, this method has high efficiency. The simulation results show that the proposed method with a superiority of 0.5% over MetaPath, 1.32% over FriendLink, and 1.8% over Katz in thef-measure criterion shows the best performance.

Group Relationship Mining of College Students Based on Predictive 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.

Improving link prediction in social networks using local and global features: a clustering-based approach

Improving link prediction in social networks using local and global features: a clustering-based approach

Network Embedding For Link Prediction in Bipartite Networks

Many social networks have a bipartite nature. Link prediction in social networks has been the focus of interest for many researchers recently. Network embedding, which maps each node in the network to a low-dimensional feature vector is used to solve many problems. The aim of this study is to investigate how network embedding enhance the link prediction performance in bipartite networks. A network embedding and a supervised learning based link prediction model has been presented for bipartite networks. The input of the supervised learning model is learned embedding vectors of node pairs obtained from network embedding method. The target feature of prediction is a binary label indicating the existence or absence of a link between these node pairs. Ensemble learning algorithms have been applied for supervised link prediction. The experiments performed on two bipartite social networks built from public datasets led promising results with 0.939 and 0.974 AUC values. Random Forest models trained with embedding vectors obtained from BiNE method achieved the highest performances.

Explainable link prediction based on multi-granularity relation-embedded representation

Existing link prediction methods focus on mining relations of nodes in terms of network structure, ignoring rich attributes of nodes. In the micro-blog social networks, text contents describe users’ diverse behaviors, which depicts ones’ multi-dimensional and multi-granularity preferences. Thus, based on users’ multi-granularity interest, we can predict and explain social relations among users. In this work, we develop an explainable social relation extraction method based on hierarchical semantic specificity matrices of user interest. First, according to users’ micro-blog contents, we model user interest subjects and design their semantic specificity matrices. Then, on the basis of interest subjects, we learn multi-dimensional and multi-granularity semantic specificity matrices with translation mechanism, which reflects user pairs’ follow relation causes from multiple aspects. Finally, by leveraging multi-granularity semantic specificity matrices, we predict and explain users’ social follow relation. To justify our proposal, we conduct extensive experiments on Sina Weibo and Tencent Weibo datasets. The experimental results demonstrate that the proposed model outperforms state-of-the-art methods in Hits, MeanRank and MRR metrics. The proposal can effectively predict users’ follow relation from the aspects of multi-granularity interest subjects, and improve the accuracy of link prediction in social networks.

A Social Network Link Prediction Method Based on Stacked Generalization

Abstract Traditional link prediction methods of social network are vulnerable to the influence of network structure and have poor generalization, and only on a small number of networks and evaluation indicators. To improve the stability and accuracy of link prediction, this paper assembles 15 similarity indexes, introduces the idea of stacking into the link prediction of complex networks, and presents a link prediction method (Logistic-regression LightGBM Stacking Link Prediction, LLSLP). Firstly, social network link prediction is regarded as a binary classification problem. Secondly, the hyper parameters of the basic model are determined by using cross-validation and grid searching; thirdly, Logistic-regression and LightGBM are integrated by stacked generalization; Finally, take 10 different networks as practical examples. The feasibility and effectiveness of the proposed method are verified by comparing 7 evaluation indicators. The experimental results show that: the proposed method is not only more than 98.71% higher than the traditional CN (Common Neighbor) and other models are 10.52% higher on average. In addition, compared with the traditional 15 link prediction algorithms, $F1- score$ value and $MCC$ (Matthews Correlation Coefficient) value is increased by 3.2% ~ 9.7% and 5.9% ~ 14% respectively. The proposed method has good accuracy and generalization. It can also be applied to recommendation system.

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

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.

Link Prediction in Social Network using Artificial Neural Network

Link Prediction in Social Network using Artificial Neural Network

Link Prediction in Facebook using Web Scrapping and Deep Learning Techniques

As Internet technologies develop continuously social networks are getting more popular day by day. People are connected with each other via virtual applications. Using the Link Prediction in social networks more people get connected, may be they are friends, may be work together at the same workplace and may be their education are. Machine learning techniques are used to analyze the link between the nodes of the network and also create a better link prediction model through deep learning. The objective of this research is to measure the performance using the different techniques to predict link between the social networks. Using deep learning, feature engineering can be reduced for link prediction. In this research, the feature based learning is used to predict the link for better performance. Dataset is obtained by scraping the profile of Facebook users and they are used along with the random forest and graph convolution neural network to measure the performance of link prediction in social networks.