Recommendation In Social Networks Research Articles

A parameterised model for link prediction using node centrality and similarity measure based on graph embedding

Link prediction is a crucial aspect of graph machine learning, with applications as diverse as disease prediction, social network recommendations, and drug discovery. It involves the prediction of potential new links between nodes within a network. Despite its importance, current models for link prediction exhibit notable limitations. Graph Convolutional Networks have shown high efficiency in link prediction across various datasets. However, they face significant challenges when applied to short-path networks and ego networks, resulting in poor performance. This issue represents a critical area of concern that our work seeks to address. This paper introduces the Node Centrality and Similarity Based Parameterised Model (NCSM), a novel method for link prediction tasks. NCSM uniquely integrates node centrality and similarity measures as edge features in a customised Graph Neural Network (GNN) layer, effectively leveraging the topological information of large networks. This model represents the first parameterised GNN-based link prediction model that considers topological information. The proposed model was evaluated on five benchmark graph datasets, each comprising thousands of nodes and edges. Experimental results highlight NCSM's superiority over existing state-of-the-art models like Graph Convolutional Networks and Variational Graph Autoencoder, as it outperforms them across various metrics and datasets. This exceptional performance can be attributed to NCSM's innovative integration of node centrality, similarity measures, and its efficient use of topological information.

Recency-based spatio-temporal similarity exploration for POI recommendation in location-based social networks

Point-of-interest (POI) recommendation is one of the primary tasks of location-based social networks (LBSNs). With user data in bulk, extracting useful information and addressing issues such as data sparsity and cold-start problems looming large in collaborative filtering become difficult. One of the plausible solutions is to incorporate contextual information into the recommendation process. In this article, we propose a Recency-based Spatio-Temporal Similarity Exploration (RSTSE) for POI recommendation that utilizes the recency-based trust estimation among the prospective neighbors of the target user. The trust level is categorized into two heads: direct trust, which can be extracted from the peer group information of the user, and indirect trust, which is measured based on venue popularity, temporal recency, radial proximity, and transitivity. The approach consists of two phases. In the incipient phase, POIs are extracted based on the preferences of potential neighbors, including the users who are recognized peers, the users with similar visiting histories in the spatial and temporal context, and the users with friend-of-friend relations. The telic phase involves Neural Collaborative Filtering (NCF) to capture the linear and non-linear user–POI interactions better. RSTSE has been evaluated on three real-world datasets, namely, Gowalla, Foursquare, and Weeplaces, and the results suggest efficacy over other state-of-the-art approaches.

Enhancing link prediction efficiency with shortest path and structural attributes

Link prediction is one of the most essential and crucial tasks in complex network research since it seeks to forecast missing links in a network based on current ones. This problem has applications in a variety of scientific disciplines, including social network research, recommendation systems, and biological networks. In previous work, link prediction has been solved through different methods such as path, social theory, topology, and similarity-based. The main issue is that path-based methods ignore topological features, while structure-based methods also fail to combine the path and structured-based features. As a result, a new technique based on the shortest path and topological features’ has been developed. The method uses both local and global similarity indices to measure the similarity. Extensive experiments on real-world datasets from a variety of domains are utilized to empirically test and compare the proposed framework to many state-of-the-art prediction techniques. Over 100 iterations, the collected data showed that the proposed method improved on the other methods in terms of accuracy. SI and AA, among the existing state-of-the-art algorithms, fared best with an AUC value of 82%, while the proposed method has an AUC value of 84%.

DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization

Graph clustering is a fundamental and challenging task in the field of graph mining where the objective is to group the nodes into clusters taking into consideration the topology of the graph. It has several applications in diverse domains spanning social network analysis, recommender systems, computer vision, and bioinformatics. In this work, we propose a novel method, DGCluster, which primarily optimizes the modularity objective using graph neural networks and scales linearly with the graph size. Our method does not require the number of clusters to be specified as a part of the input and can also leverage the availability of auxiliary node level information. We extensively test DGCluster on several real-world datasets of varying sizes, across multiple popular cluster quality metrics. Our approach consistently outperforms the state-of-the-art methods, demonstrating significant performance gains in almost all settings.

Efficient High-Quality Clustering for Large Bipartite Graphs

A bipartite graph contains inter-set edges between two disjoint vertex sets, and is widely used to model real-world data, such as user-item purchase records, author-article publications, and biological interactions between drugs and proteins. k-Bipartite Graph Clustering (k-BGC) is to partition the target vertex set in a bipartite graph into k disjoint clusters. The clustering quality is important to the utility of k-BGC in various applications like social network analysis, recommendation systems, text mining, and bioinformatics, to name a few. Existing approaches to k-BGC either output clustering results with compromised quality due to inadequate exploitation of high-order information between vertices, or fail to handle sizable bipartite graphs with billions of edges. Motivated by this, this paper presents two efficient k-BGC solutions, HOPE and HOPE+, which achieve state-of-the-art performance on large-scale bipartite graphs. HOPE obtains high scalability and effectiveness through a new k-BGC problem formulation based on the novel notion of high-order perspective (HOP) vectors and an efficient technique for low-rank approximation of HOP vectors. HOPE+ further elevates the k-BGC performance to another level with a judicious problem transformation and a highly efficient two-stage optimization framework. Two variants, HOPE+ (FNEM) and HOPE+ (SNEM) are designed when either the Frobenius norm or spectral norm is applied in the transformation. Extensive experiments, comparing HOPE and HOPE+ against 13 competitors on 10 real-world datasets, exhibit that our solutions, especially HOPE+, are superior to existing methods in terms of result quality, while being up to orders of magnitude faster. On the largest dataset MAG with 1.1 billion edges, HOPE+ is able to produce clusters with the highest clustering accuracy within 31 minutes, which is unmatched by any existing solution for k-BGC.

High-order spatial connectivity mining over neural graph collaborative filtering for POI recommendation in location-based social networks

High-order spatial connectivity mining over neural graph collaborative filtering for POI recommendation in location-based social networks

Design and Development of Powerful Neuroevolution Based Optimized GNNBiLSTM Model for Consumer Behaviour and Effective Recommendation in Social Networks

Design and Development of Powerful Neuroevolution Based Optimized GNNBiLSTM Model for Consumer Behaviour and Effective Recommendation in Social Networks

Enhancing group recommender systems: A fusion of social tagging and collaborative filtering for cohesive recommendations

AbstractThis study examines the challenges and opportunities of using group recommendation systems in an information overload scenario. Social network recommendation systems are increasingly important because they deliver users customized choices. Most existing solutions are geared for single users, making it difficult to propose for a group with different interests. This paper analyses group recommendation systems and exposes their flaws. This study tested whether the suggested approach outperforms the one without tagging information in recall, precision, and user satisfaction. Empirical evidence indicates that the algorithm exhibits appropriate levels of reliability and accuracy compared to conventional methods. The proposed approach has the potential to substantially enhance the existing state of social network group recommendation systems, thereby facilitating users in their quest to identify and participate in groups that align with their preferences.

Attentive Implicit Relation Embedding for Event Recommendation in Event-Based Social Network

The event-based social network (EBSN) is a new type of social network that combines online and offline networks, and its primary goal is to recommend appropriate events to users. Most studies do not model event recommendations on the EBSN platform as graph representation learning, nor do they consider the implicit relationship between events, resulting in recommendations that are not accepted by users. Thus, we study graph representation learning, which integrates implicit relationships between social networks and events. First, we propose an algorithm that integrates implicit relationships between social networks and events based on a multiple attention model. The graph structure that integrates implicit relationships between social networks and events is divided into user modeling and event modeling: modeling the interactive information of user events, user social relationships, and implicit relationships between users in user modeling; modeling user information and implicit relationships between events in event modeling; and deeply mining high-level transfer relationships between users and events. Then, the user modeling and event modeling models are fused using a multiattention joint learning mechanism to capture the different impacts of social and implicit relationships on user preferences, improving the recommendation quality of the recommendation system. Finally, the effectiveness of the proposed algorithm is verified in real datasets.

Exploiting social capital for improving personalized recommendations in online social networks

This article presents a novel approach to enhancing personalized recommendations in online social networks by exploiting the concept of Social Capital. Recognizing the challenges posed by information overload on platforms like Twitter, the proposed method integrates user interactions and features on social media with the concept of Social Capital to generate more relevant recommendations. The model incorporates a multifaceted analysis of user data, including the user’s reputation, influence, and engagement strength, alongside tweets’ recency, diversity, and context scores, to calculate a comprehensive Social Capital Score for each recommendation. An extensive offline evaluation of the model demonstrates its effectiveness in improving the personalization and relevance of recommendations. The findings highlight the significant role of incorporating Social Capital in recommender systems, contributing to a more engaging and informative user experience on social media platforms. This study provides valuable insights into the potential benefits and limitations of this approach, advancing the field of recommender systems and enhancing our understanding of the role of social capital in shaping user behavior and preferences.

High-Performance Technique for Item Recommendation in Social Networks using Multiview Clustering

Recommender Systems have been widely employed in information systems over the past few decades, making it easier for each user to choose their own products based on their past behaviour. Data mining tasks and visualization tools regularly use clustering techniques in the scientific and commercial arenas. It has been shown that clustering-based methods are effective and scalable to big data sets. The accuracy and coverage of clustering-based recommender systems are, however, somewhat low. In this paper, we suggest an improved multi-view clustering method for the recommendation of items in social networks to overcome these problems. To create better partitions, the artificial Bees colony optimization algorithm (ABC) is first used to improve the initial medoids’ selection. After that, users are clustered iteratively using views of both rating patterns as well as social information using multiview clustering (MVC) (i.e. trust and friendships). Ultimately, a framework is suggested for evaluating the various options. This research study suggests a novel MVC clustering approach using the ABC optimization technique. The proposed ABC-MVC algorithm’s usefulness in terms of enhancing accuracy is demonstrated by experimental findings performed on a real-world dataset and it is observed that it performs better than the pre-existing techniques and baselines.

Exploiting User Mobility for Time-aware POI Recommendation in Social Networks

Location-Based Social Networks (LBSNs) have recently gained increasing popularity. Timeaware Point-of-Interest(POI) recommendation is one of the most important location-aware services, which can recommend locations to a target user at the specific time based on check-in history. However, current techniques ignore user’s mobility within a region, which plays a vital role in the POI chosen decision. Most of existing algorithms fail to capture the latent relations behind the temporal factors and geographical influence. In this paper, we propose a novel hybrid Time-aware POI Recommendation model based on User Mobility: TPR-UM, which improves the POI recommendation accuracy greatly. More specifically, by introducing the implicit region factor, we capture users’ mobility through collective user actions and geographical properties of locations. We generate the check-in patterns based on different time intervals and different regions to exploit the latent relations between temporal factors and geographical influence. Finally, we conduct comprehensive experiments on two real-world datasets, Gowalla and Foursquare. The experimental results demonstrate that our hybrid method is effective and outperforms other state-of-the-art algorithms in terms of precision, recall and Fβ measurement.

A self-attention model with contrastive learning for online group recommendation in event-based social networks

A self-attention model with contrastive learning for online group recommendation in event-based social networks

Social Network Recommendations for Friends with Neo4j Graph Database

In recent years, along with the development of the internet, the number of social network users is increasing day by day. Through social networks, users can share, exchange information or make friends with each other. However, with new relationships, users often have a need to assess the credibility of a new friend before making friends on social networks. This paper proposes a friend recommendation system on social networks based on how to calculate the reliability between users. The recommendation system is implemented using the Neo4j graph database. The results of the truth algorithm proposed in this paper are higher than other similar algorithms.

Social Network Recommendations for Friends with Neo4j Graph Database

In recent years, along with the development of the internet, the number of social network users is increasing day by day. Through social networks, users can share, exchange information or make friends with each other. However, with new relationships, users often have a need to assess the credibility of a new friend before making friends on social networks. This paper proposes a friend recommendation system on social networks based on how to calculate the reliability between users. The recommendation system is implemented using the Neo4j graph database. The results of the truth algorithm proposed in this paper are higher than other similar algorithms.

A distributed streaming framework for edge–cloud triangle counting in graph streams

The triangle counting problem in graph streams has been extensively studied in social network analysis, recommendation systems, user portraits and other fields. However, cloud computing based streaming algorithms cause high bandwidth occupation and long transmission latency due to limited bandwidth of the cloud. Recently, edge computing is promising to overcome the issue of transmitting large-scale data for cloud computing. However, directly applying edge computing in streaming triangle counting will reduce the accuracy of the triangle count estimation, due to the limitation of local computing at the edge network. We term the cooperations between edge computing and cloud computing for streaming triangle counting as edge–cloud triangle counting in graph streams. In this paper, we first propose a streaming framework for edge–cloud triangle counting in graph streams. Then, we propose a streaming triangle counting algorithm called Trie-based Edge Compression (TbEC) by using the binary trie at the edge network that enables lossless compression and efficient transmission to the cloud. In addition, to extend our algorithms for triangle counting in multigraphs, we present a dual deduplication strategy collaboratively using the trie-based data structure and a Bloom Filter. Our experiments with real-world datasets show that TbEC is (a) Accurate: yielding up to 3.35×more accurate smaller estimation error than the state-of-the-art distributed streaming algorithm, (b) Fast: yielding up to 10.59×faster than the state-of-the-art distributed streaming algorithm, (c) Scalable: scaling linearly with the number of edges in the input graph stream.

Trust-aware spatial–temporal feature estimation for next POI recommendation in location-based social networks

Trust-aware spatial–temporal feature estimation for next POI recommendation in location-based social networks

A survey of graph neural network based recommendation in social networks

With the widespread popularization of social network platforms, user-generated content and other social network data are growing rapidly. It is difficult for social users to select interested contents from the numerous social data. To alleviate information overload problem and enhance overall user experience of social networks, recommendation systems relying on historical behavioural data and social friendship relations of users, are widely used in social networks. Although researches on social recommendations have been conducted in recent years, recommendation systems of social networks still suffer from several challenges, such as data sparsity and lower performance. Since graph neural network has huge advantages in graph data learning by aggregating neighbors representations of the central node, it has been gathering pace in recent years. In this survey, we review graph neural network based literature for solving recommendation problems in social networks. We first introduce backgrounds of graph neural network and recommendation systems in social networks. Then, for different types of recommendation problems in social networks, we review different graph neural network based recommendation methods briefly. In particular, we first review GNN-based methods for general social recommendation and then review GNN-based methods for different social recommendation scenarios (such as friend recommendation and point-of-interest recommendation). Finally, we briefly discuss promising future directions of the graph neural network based recommendation in social networks.

Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees

Representation learning over dynamic graphs is critical for many real-world applications such as social network services and recommender systems. Temporal graph neural networks (T-GNNs) are powerful representation learning methods and have achieved remarkable effectiveness on continuous-time dynamic graphs. However, T-GNNs still suffer from high time complexity, which increases linearly with the number of timestamps and grows exponentially with the model depth, causing them not scalable to large dynamic graphs. To address the limitations, we propose Orca, a novel framework that accelerates T-GNN training by non-trivially caching and reusing intermediate embeddings. We design an optimal cache replacement algorithm, named MRU, under a practical cache limit. MRU not only improves the efficiency of training T-GNNs by maximizing the number of cache hits but also reduces the approximation errors by avoiding keeping and reusing extremely stale embeddings. Meanwhile, we develop profound theoretical analyses of the approximation error introduced by our reuse schemes and offer rigorous convergence guarantees. Extensive experiments have validated that Orca can obtain two orders of magnitude speedup over the state-of-the-art baselines while achieving higher precision on large dynamic graphs.

TrustDL: Use of trust-based dictionary learning to facilitate recommendation in social networks

Collaborative filtering (CF) is a widely applied method to perform recommendation tasks in a wide range of domains and applications. Dictionary learning (DL) models, which are highly important in CF-based recommender systems (RSs), are well represented by rating matrices. However, these methods alone do not resolve the cold start and data sparsity issues in RSs. We observed a significant improvement in rating results by adding trust information on the social network. For that purpose, we proposed a new dictionary learning technique based on trust information, called TrustDL, where the social network data were employed in the process of recommendation based on structural details on the trusted network. TrustDL sought to integrate the sources of information, including trust statements and ratings, into the recommendation model to mitigate both problems of cold start and data sparsity. It conducted dictionary learning and trust embedding simultaneously to predict unknown rating values. In this paper, the dictionary learning technique was integrated into rating learning, along with the trust consistency regularization term designed to offer a more accurate understanding of the feature representation. Moreover, partially identical trust embedding was developed, where users with similar rating sets could cluster together, and those with similar rating sets could be represented collaboratively. The proposed strategy appears significantly beneficial based on experiments conducted on four frequently used datasets: Epinions, Ciao, FilmTrust, and Flixster.