Link prediction on signed social networks based on latent space mapping

In recent years, the use of social media networks has increased. With the rise in social network usage, social media businesses have begun to place a greater emphasis on large-scale social network analysis. As a result, several components of the signed network have been studied and analysed. Link prediction is a crucial aspect of this signed network analysis. The majority of previous study focused on forecasting the network’s good aspects, such as favourable user connections, but the negative component(i.e., Negative Link) is just as significant as the positive (i.e., positive link). It enhances the performance of several current positive link analysis programmes. As a result, we concentrate on predicting negative ties from a social network that contains both positive and negative interactions in this research. To improve prediction precision, we use social theories such as balancing and status theory. We utilise the XGBoost classifier to determine if a particular missing link is positive or negative. Finally, we use accuracy and F1 measure measures to assess our proposed model.

Various types of social relationships, such as friends and foes, can be represented as signed social networks (SNs) that contain both positive and negative links. Although many community detection (CD) algorithms have been proposed, most of them were designed primarily for networks containing only positive links. Thus, it is important to design CD algorithms which can handle large-scale SNs. To this purpose, we first extend the original similarity to the signed similarity based on the social balance theory. Then, based on the signed similarity and the natural contradiction between positive and negative links, two objective functions are designed to model the problem of detecting communities in SNs as a multiobjective problem. Afterward, we propose a multiobjective evolutionary algorithm, called MEAs-SN. In MEAs-SN, to overcome the defects of direct and indirect representations for communities, a direct and indirect combined representation is designed. Attributing to this representation, MEAs-SN can switch between different representations during the evolutionary process. As a result, MEAs-SN can benefit from both representations. Moreover, owing to this representation, MEAs-SN can also detect overlapping communities directly. In the experiments, both benchmark problems and large-scale synthetic networks generated by various parameter settings are used to validate the performance of MEAs-SN. The experimental results show the effectiveness and efficacy of MEAs-SN on networks with 1000, 5000, and 10,000 nodes and also in various noisy situations. A thorough comparison is also made between MEAs-SN and three existing algorithms, and the results show that MEAs-SN outperforms other algorithms.

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

Link prediction is one of hot research topics in social network analysis. Link prediction problem is to find a small set of node pairs in the networks that are not directly connected, but will be very likely to be connected in the future. To improve the prediction accuracy, many works have attempted to consider the community information, if available, in the social network structure. One common strategy of the prior community-aware link prediction algorithms is that they devised a sort of unified link prediction formulation that simply includes a premium term to express whether a link is structurally in the same community or not. However, since the formulation of the premium term relies on the structural formation of communities only, it cannot take into account the fact that the communities in different social networks, though they form almost identical community structures, can make different levels of influence on the link prediction. To cope with this limitation, we propose an adaptive approach, in which we use two separate link predictions depending on inter or intra links in community, and then balance the links based on the degree of community influence on link prediction. In the meantime, we provide a set of experimental results to show how much the proposed approach is effective.

Interactomes such as Protein interaction networks have many undiscovered links between entities. Experimental verification of every link in these networks is prohibitively expensive, and therefore computational methods to direct the search for possible links are of great value. The problem of finding undiscovered links in a network is also referred to as the link prediction problem. A popular approach for link prediction has been to formulate it as a binary classification problem in which class labels indicate the existence or absence of a link (we refer to these as positive links or negative links respectively) between a pair of nodes in the network. Researchers have successfully applied such supervised classification techniques to determine the presence of links in protein interaction networks. However, it is quite common for protein-protein interaction (PPI) networks to have a large proportion of undiscovered links. Thus, a link prediction approach could incorrectly treat undiscovered positive links as negative links, thereby introducing a bias in the learning. In this paper, we propose to denoise the class of negative links in the training data via a Gaussian process anomaly detector. We show that this significantly reduces the noise due to mislabelled negative links and improves the resulting link prediction accuracy. We evaluate the approach by introducing synthetic noise into the PPI networks and measuring how accurately we can reconstruct the original PPI networks using classifiers trained on both noisy and denoised data. Experiments were performed with five different PPI network datasets and the results indicate a significant reduction in bias due to label noise, and more importantly, a significant improvement in the accuracy of detecting missing links via classification.

Detecting communities in complex networks accurately is a prime challenge, preceding further analyses of network characteristics and dynamics. Until now, community detection took into account only positively valued links, while many actual networks also feature negative links. We extend an existing Potts model to incorporate negative links as well, resulting in a method similar to the clustering of signed graphs, as dealt with in social balance theory, but more general. To illustrate our method, we applied it to a network of international alliances and disputes. Using data from 1993-2001, it turns out that the world can be divided into six power blocs similar to Huntington's civilizations, with some notable exceptions.

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.

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

In today’s world, users typically take to online social media to express their opinions, which can inherently be both positive and negative. In fact, online social networks can be best modeled as signed networks, where opinions in the form of positive and negative links can exist between users, such as our friends and foes (e.g., “unfriended” users), respectively. Furthermore, users can also express their opinions to content generated by others through online social interactions, such as commenting or rating. Intuitively, these two types of opinions in the form of links and interactions should be related. For example, users’ interactions are likely to be positive (or negative) to those they have positively (or negatively) established links with. Similarly, we tend to establish positive (or negative) links with those whose generated content we frequently positively (or negatively) interact with online. Hence, in this paper, we first verify these assumptions by understanding the correlation between these two types of opinions from both a local and global perspective. Then, we propose a framework that jointly solves the link and interaction polarity prediction problem based on our newly found understanding of how these two problems are correlated. We ultimately perform experiments on a real-world signed network to demonstrate the effectiveness of our proposed approach to help mitigate both the data sparsity and cold-start problems found in the two tasks of link and interaction polarity prediction.

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.

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

Dynamic networks are inherently evolutionary in nature where the characteristics, behaviour and activities of their constituents (i.e., actors and links) change temporally. In regards to time-evolving model in social network analyses, link prediction supports the understanding of the rationale behind the underlying growth mechanisms of social networks. Mining the temporal patterns of actor-level evolutionary changes in regards to their network neighbourhood, and community-based dynamic information may suitably be used for the purpose of dynamic link prediction. Considering the evolutionary aspects of mesoscale network structure (i.e., community), this study sought to build dynamic similarity metrics or dynamic features to measure similarity/proximity between actor-pairs. A supervised link prediction model was set out where these features were employed as instance features and performances were compared against an existing topological similarity-based link prediction metric. High-performance scores achieved by these dynamic feature, examined in this study, represent them as prospective candidates not only for dynamic link prediction task but also understanding of underlying network evolution mechanism.

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.

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.

Social network analysis has recently attracted lots of attention among researchers due to its wide applicability in capturing social interactions. Link prediction, related to the likelihood of having a link between two nodes of the network that are not connected, is a key problem in social network analysis. Many methods have been proposed to solve the problem. Among these methods, similarity-based methods exhibit good efficiency by considering the network structure and using as a fundamental criterion the number of common neighbours between two nodes to establish structural similarity. High structural similarity may suggest that a link between two nodes is likely to appear. However, as shown in the paper, the number of common neighbours may not be always sufficient to provide comprehensive information about structural similarity between a pair of nodes. To address this, a neighbourhood vector is first specified for each node. Then, a novel measure is proposed to determine the similarity of each pair of nodes based on the number of common neighbours and correlation between the neighbourhood vectors of the nodes Experimental results, on a range of different real-world networks, suggest that the proposed method results in higher accuracy than other state-of-the-art similarity-based methods for link prediction.