Community-Adaptive Link Prediction

Link prediction in multilayer networks using weighted reliable local random walk algorithm

Toward link prediction in directed social networks based on common interest and local community

Social network analysis is an approach to the study of social structures. One of the important fields in social networks analyses is link prediction. Link prediction tries to reach an appropriate answer to this question: what kinds of interaction among members of a network would possible form in future, given a snapshot of the network in current time. The main purpose of this paper is to boost the performance of similarity based link prediction methods by using community information. This information is derived from the structure of the graph, based on the number of community levels that two vertices have in common, in a hierarchical representation of communities. To evaluate the performance of the proposed method, four datasets are used as benchmark. The results suggest that the information of communities often increases the efficiency and accuracy of link prediction.

In recent times there has been an exponential growth in Social networks. Virtual communication has become the order of the day with the Online social Networks catering to this need. The huge amount of activity occurring on social media and the data that has been generated on social networks has kindled the interest of researchers to work on the Social Network Analytics (SNA). There are a variety of problems addressed in SNA, Link Prediction is one among them. The problem of Link Prediction is to infer a connection in a social network which could occur at a later point in time. This paper proposes an algorithm to infer future ties on social networks based on nodes which communicate more frequently with each other. The algorithm was tested on Data taken from real world networks and it has outperformed the existing baseline methods.

The detection of communities is an important problem in social network analysis, which has applications in various domains like sociology, biology, computer science, and marketing. In this context, genetic algorithms have proven to be effective in detecting communities by optimizing the modularity score of the network. The proposed work in this research paper uses an elitism-based genetic algorithm with some modified crossover and mutation techniques to detect communities in social networks. The proposed methodology incorporates the concepts of elitism, N-point crossover, and inverse mutation to enhance the effectiveness of genetic algorithms in solving optimization problems. The idea introduced in this article significantly extends the current understanding of optimization and evolutionary algorithms. We present an advanced methodology that leverages various genetic operators to improve the performance of a genetic algorithm in solving community detection problems in complex networks. Numerous research papers have extensively showcased the practicality of evolutionary and swarm-based algorithms in addressing real-world problems across diverse domains like viral marketing, link prediction, influence maximization, political polarization, etc. Hybridizing these algorithms with other optimization techniques has improved the performance and convergence speed, leading to enhanced optimization outcomes.

Social networks are structures that are used to model complex networks in different environments. The desire of reaching useful information by studying complex networks has made social network analysis an important research topic today. One of the most interesting subjects of social network analysis is link prediction. It estimates potential future connections by using the current state of the network. In this study, link prediction is made in bipartite graphs which is one of the social network structures. For this study, the network is first constructed with question and advise (generally disease) made on online health forum sites. Online health forum is a place where user can get his/her medical questions answered by real health professionals and other forum members. Then, symptoms of diseases are obtained by analyzing questions on online forum sites. Thus, a bipartite network consisting of questions and diseases corresponding to the obtained symptom data is constructed. In this network, link prediction has been made with internal links method. The results of the method have been compared with four of the other link prediction methods and it was found that this method has better performance than the other methods.

Literature have focused on studying the apparent and latent interactions within social graphs as an n‐ary operation, which yields binary outputs comprising positives (friends, likes, etc.) and negatives (foes, dislikes, etc.). Inasmuch as interactions constitute the bedrock of any given social network (SN) structure; there exist scenarios where an interaction, which was once considered a positive, transmutes into a negative as a result of one or more indicators which have affected the interaction quality. At present, this transmutation has to be manually executed by the affected actors in the SN. These manual transmutations can be quite inefficient, ineffective, and a mishap might have been incurred by the constituent actors and the SN structure prior to a resolution. Our problem statement aims at automatically flagging positive ties that should be considered for breakups or rifts (negative‐tie state), as they tend to pose potential threats to actors and the SN. Therefore, we have proposed ClasReg: a unique framework capable of breakup and link predictions.

On-line social networks (OSNs) often contain many different types of relationships between users. When studying the structure of OSNs such as Facebook, two of the most commonly studied networks are friendship and interaction networks. The link prediction problem in friendship networks has been heavily studied. There has also been prior work on link prediction in interaction networks,independent of friendship networks. In this paper, we study the predictive power of combining friendship and interaction networks. We hypothesize that, by leveraging friendship networks, we can improve the accuracy of link prediction in interaction networks. We augment several interaction link prediction algorithms to incorporate friendships and predicted friendships. From experiments on Facebook data, we find that incorporating friendships into interaction link prediction algorithms results in higher accuracy, but incorporating predicted friendships does not when compared to incorporating current friendships.

Currently, online social networks and social media have become increasingly popular showing an exponential growth. This fact have attracted increasing research interest and, in turn, facilitating the emergence of new interdisciplinary research directions, such as social network analysis. In this scenario, link prediction is one of the most important tasks since it deals with the problem of the existence of a future relation among members in a social network. Previous techniques for link prediction were based on structural (or topological) information. Nevertheless, structural information is not enough to achieve a good performance in the link prediction task on large-scale social networks. Thus, the use of additional information, such as interests or behaviors that nodes have into their communities, may improve the link prediction performance. In this paper, we analyze the viability of using a set of simple and non-expensive techniques that combine structural with community information for predicting the existence of future links in a large-scale online social network, such as Twitter. Twitter, a microblogging service, has emerged as a useful source of informative data shared by millions of users whose relationships require no reciprocation. Twitter network was chosen because it is not well understood, mainly due to the occurrence of directed and asymmetric links yet. Experiments show that our proposals can be used efficiently to improve unsupervised and supervised link prediction task in a directed and asymmetric large-scale network.

Link prediction problem in social networks has received significant interest in the recent past from the researchers in diverse fields. Understanding and analyzing the present links in the social network or any complex networks either to understand their evolution or to predict the future possible links from the existing network (or links) forms the interesting link prediction problem. Link prediction based on Firefly optimization algorithm is proposed in this paper for social networks. The proposed algorithm is executed on a logical graph similar to social network and tested over real networks taking the benchmark data sets. Experimental values are compared with the other methods existing in the literature. From the comparison we can see that the proposed method performs better in terms of precision over the other methods.

Combining contextual, temporal and topological information for unsupervised link prediction in social networks

The automated analysis of social networks has become an important problem due to the proliferation of social networks, such as LiveJournal, Flickr and Facebook. The scale of these social networks is massive and continues to grow rapidly. An important problem in social network analysis is proximity estimation that infers the closeness of different users. Link prediction, in turn, is an important application of proximity estimation. However, many methods for computing proximity measures have high computational complexity and are thus prohibitive for large-scale link prediction problems. One way to address this problem is to estimate proximity measures via low-rank approximation. However, a single low-rank approximation may not be sufficient to represent the behavior of the entire network. In this paper, we propose Multi-Scale Link Prediction (MSLP), a framework for link prediction, which can handle massive networks. The basic idea of MSLP is to construct low-rank approximations of the network at multiple scales in an efficient manner. To achieve this, we propose a fast tree-structured approximation algorithm.

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.

Link prediction is a very well studied problem as it has applications in different areas. Many algorithms have been presented in the literature for link prediction problem. The link prediction problem can be explained as follows: given the topology of graph G at a certain time t, we need to predict the topology of the graph at time t′ where t′ > t. The techniques used for link prediction are categorized as follows: nodes based techniques, Link based techniques and path based techniques. There are some other techniques that use meta-approaches. In this paper we present a new algorithm for link prediction on social networks. The algorithm tries to identify nodes that may get deleted by time t′ and use this information to predict new links that might appear in the future. Our tests show that our algorithm shows better result for link prediction on social networks compared to the previous algorithms. We had implemented our algorithm in C# on Pentium Core 2 Duo processor. The data used for testing is Gnutella peer to peer network.

In some online Social Network Services, users are allowed to label their relationship with others, which can be represented as links with signed values. The link prediction problem is to estimate the values of unknown links by the information from the social network. A lot of similarity based metrics and machine learning based methods are proposed. Most of these methods are based on the network topological and node states. In this paper, by considering the information from user comment and sentiment lexicon, our methods improved the performances of link prediction for both similarity based metrics and machine learning based methods.KeywordsLink predictionSocial networksLink network structureUser commentsSentiment lexicon