Real-world Social Network Datasets Research Articles

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.

Social network node pricing based on graph autoencoder in data marketplaces

Data have become a valuable digital resource. It has in turn precipitated the emergence of big data marketplaces. For social network date in the marketplaces, each node should be priced according to its influence. The key challenge is that deep learning based pricing models require initial cascade graphs as inputs to predict influence, which cannot be obtained while pricing nodes. Furthermore, node pricing must enhance purchase intentions while being consistent with their influence. To address these challenges, a node-pricing framework is proposed, in which market price is determined based on the predicted influence. In this framework, corrections are performed by using a graph autoencoder (GAE). The corrections are used to augment the neighborhood subgraph and facilitate the extraction of valid sequence features, which are then used to predict influence. An approximate Shapley value for node influence is used to evaluate the price of the nodes. A multi-perspective pricing approach is further investigated, where consumer utility and the approximate Shapley value for influence are the objectives. An inflection point is chosen on the Pareto frontier to select a price that enhances consumer utility. Extensive experiments were conducted on two real-world social network datasets. The results indicate that our performance is higher than DeepCas by 10.38% in Twitter and 9.64% in Weibo . The price output by our framework is consistent with the nodes’ social marketing value while maximizing consumer utility.

Implicit Negative Link Prediction With a Network Topology Perspective

Sign prediction in signed social networks is a new research direction in the field of social relation mining, which reveals underlying links between users. Traditional sign prediction research focuses on the prediction of positive signs and neglects the mining of potential implicit links, and there is little research on negative sign prediction. To address these problems, we propose a two-stage model that uses implicit link detection and link sign prediction. First, we use the preference attachment closeness degree (PACD) to predict possible implicit links by adding a measure of relationship closeness to the traditional link prediction algorithm (PA). Next, we propose a negative link sign prediction (Ne-LP) method to predict relation types through multidimensional negative sign-related features, including those of nodes, user similarity, and structural balance, and merge them by a logistic regression model. Finally, we evaluate PACD and Ne-LP through extensive experiments on three real-world social network datasets, whose results demonstrate that the method can effectively mine implicit relations and accurately predict negative links.

Learning Hierarchical Spatial Tasks with Visiting Relations for Next POI Recommendation

Sparsity is an established problem for the next Point-of-Interest (POI) recommendation task, where it hinders effective learning of user preferences from the User-POI matrix. However, learning multiple hierarchically related spatial tasks, and visiting relations between users and POIs, can help to alleviate this sparsity problem. In this article, we propose our Hierarchical Multi-Task Graph Recurrent Network (HMT-GRN) approach, which alleviates the sparsity problem by learning different User-Region matrices of lower sparsities in a multi-task setting. We then perform a Hierarchical Beam Search (HBS) on the different region and POI distributions to hierarchically reduce the search space with increasing spatial granularity and predict the next POI. Our HBS provides efficiency gains by reducing the search space, resulting in speedups of 5 to 7 times over an exhaustive approach. In addition, we propose a selectivity layer to predict if the next POI has been visited before by the user to balance between personalization and exploration. Further, we propose a novel Joint Triplet Loss Learning (JTLL) module to learn visited and unvisited relations between users and POIs for the recommendation task. Experimental results on two real-world Location-Based Social Network (LBSN) datasets show that our proposed approach significantly outperforms baseline and the state-of-the-art methods.

User Re-Identification via Confusion of the Contrastive Distillation Network and Attention Mechanism.

With the rise of social networks, more and more users share their location on social networks. This gives us a new perspective on the study of user movement patterns. In this paper, we solve the trajectory re-identification task by identifying human movement patterns and then linking unknown trajectories to the user who generated them. Existing solutions generally focus on the location point and the location point information, or a single trajectory, and few studies pay attention to the information between the trajectory and the trajectory. For this reason, in this paper, we propose a new model based on a contrastive distillation network, which uses a contrastive distillation model and attention mechanisms to capture latent semantic information for trajectory sequences and focuses on common key information between pairs of trajectories. Combined with the trajectory library composed of historical trajectories, it not only reduces the number of candidate trajectories but also improves the accuracy of trajectory re-identification. Our extensive experiments on three real-world location-based social network (LBSN) datasets show that our method outperforms existing methods.

Social Media Based Deep Auto-Encoder Model for Clinical Recommendation

One of the most actively studied topics in modern medicine is the use of deep learning and patient clinical data to make medication and ADR recommendations. However, the clinical community still has some work to do in order to build a model that hybridises the recommendation system. As a social media learning based deep auto-encoder model for clinical recommendation, this research proposes a hybrid model that combines deep self-decoder with Top n similar co-patient information to produce a joint optimisation function (SAeCR). Implicit clinical information can be extracted using the network representation learning technique. Three experiments were conducted on two real-world social network data sets to assess the efficacy of the SAeCR model. As demonstrated by the experiments, the suggested model outperforms the other classification method on a larger and sparser data set. In addition, social network data can help doctors determine the nature of a patient's relationship with a co-patient. The SAeCR model is more effective since it incorporates insights from network representation learning and social theory.

GAC: A deep reinforcement learning model toward user incentivization in unknown social networks

In recent years, many applications have deployed incentive mechanisms to promote users’ attention and engagement. Most incentive mechanisms determine specific incentive values based on users’ attributes (e.g., preferences), while such information is unavailable in many real-world applications. Meanwhile, due to budget restrictions, realizing successful incentivization for all users can be challenging to complete. In this light, we consider leveraging social influence to maximize the incentivization result. We can directly incentivize influential users to affect more users, so the cost of incentivizing these users can be decreased. However, identifying influential users in a social network requires complete information about influence strength among users, which is impractical to acquire in real-world situations. In this research, we propose an end-to-end reinforcement learning-based framework, called Geometric Actor–Critic (GAC), to tackle the abovementioned problem. The proposed approach can realize effective incentive allocation without having prior knowledge about users’ attributes. Three real-world social network datasets have been adopted in the experiments to evaluate the performance of GAC. The experimental results indicate that GAC can learn and apply effective incentive allocation policies in unknown social networks and outperform existing incentive allocation approaches.

User Trust Inference in Online Social Networks: A Message Passing Perspective

Online social networks are vital environments for information sharing and user interactivity. To help users of online social services to build, expand, and maintain their friend networks or webs of trust, trust management systems have been deployed and trust inference (or more generally, friend recommendation) techniques have been studied in many online social networks. However, there are some challenging issues obstructing the real-world trust inference tasks. Using only explicit yet sparse trust relationships to predict user trust is inefficient in large online social networks. In the age of privacy-respecting Internet, certain types of user data may be unavailable, and thus existing models for trust inference may be less accurate or even defunct. Although some less interpretable models may achieve better performance in trust prediction, the interpretability of the models may prevent them from being adopted or improved for making relevant informed decisions. To tackle these problems, we propose a probabilistic graphical model for trust inference in online social networks in this paper. The proposed model is built upon the skeleton of explicit trust relationships (the web of trust) and embeds various types of available user data as comprehensively-designed trust-aware features. A message passing algorithm, loop belief propagation, is applied to the model inference, which greatly improves the interpretability of the proposed model. The performance of the proposed model is demonstrated by experiments on a real-world online social network dataset. Experimental results show the proposed model achieves acceptable accuracy with both fully and partially available data. Comparison experiments were conducted, and the results show the proposed model’s promise for trust inference in some circumstances.

Multi-attribute based influence maximization in social networks: Algorithms and analysis

The most valuable feature of social networks is that they can generate contents for users and spread them quickly on the network, which is a very important platform for viral marketing. Most of the related work on viral marketing focuses on the spread of single information, while a product may associate with multiple attributes in real life. Information about multiple attributes of a product propagates in the social networks simultaneously and independently. The attribute information that a user receives will determine whether he would purchase the product or not. We extend the traditional single information influence maximization problem to the multi-attribute based influence maximization problem. We also present the Multi-dimensional IC model (MIC model) for the proposed problem, then formulate the problem as the Multi-attribute based Influence Maximization Problem (MIMP). The objective function for MIMP is proved to be non-submodular, then we solve the problem with two different algorithms: the Sandwich Algorithm and the Supermodular Algorithm, whose solutions can get a max{f(SU)f‾(SU),f_(SL⁎)f(So⁎)}(1−1/e) approximation ratio and an 1/(d+2) approximation ratio to the optimal solution, respectively. Experiments based on the real world social network datasets verify the effectiveness and correctness of our proposed solutions.

Union acceptable profit maximization in social networks

Online social network has deeply changed our lives, such as the style of communication and business, and hence promotes a lot of researches in social influence. The prior works in social influence mainly consider the influence from the view of individuals. However, in many cases, influencing the most of members of an important group such as the board of directors in a company can bring bigger profit than directly influencing the individuals of the company. We call such high profit group which obeys the vote rule as an union, different from existed targeted influence model, we consider such scenarios to make union acceptable and propose the union acceptable profit problem (UAPM) to choose seeds to maximize the union-acceptable profit, i.e., maximize the probability of the union being acceptable. The objective of profit in UAPM is #P-hard, and not submodularity or supmodularity. To solve the problem, we propose an efficient estimation method for the objective and design a heuristic algorithm and further a data-driven β(1−1ϵ)-approximation algorithm where β is the data-driven parameter which is related to the input data. At last we evaluate the performance of the algorithms we proposed on effectiveness and efficiency by the experiments in real-world social network datasets.

Infer-AVAE: An attribute inference model based on adversarial variational autoencoder

User attributes, such as gender and education, face severe incompleteness in social networks. Attribute inference aims to infer users’ missing attribute labels based on observed data to make this valuable data usable for downstream tasks like user profiling and personalized recommendation. Recently, variational autoencoder (VAE), an end-to-end deep generative model, has shown promising performance by handling the problem in a semi-supervised way. However, VAEs can easily suffer from over-fitting and over-smoothing when applied to attribute inference. Specifically, VAE implemented with multi-layer perceptron (MLP) can only reconstruct input data but fail to infer missing parts. While using the trending graph neural networks (GNNs) as encoder has the problem that GNNs aggregate redundant information from the neighborhood and generate indistinguishable user representations, known as over-smoothing. In this paper, we propose an attribute Inference model based on Adversarial VAE (Infer-AVAE) to cope with these issues. Specifically, to overcome over-smoothing, Infer-AVAE unifies MLP and GNNs in the encoder to learn positive and negative latent representations respectively. Meanwhile, an adversarial network is trained to distinguish the two representations, and GNNs are trained to aggregate less noise for more robust representations through adversarial training. Finally, to relieve over-fitting, mutual information constraint is introduced as a regularizer for the decoder to make better use of auxiliary information in representations and generate outputs not limited by observations. We evaluate our model on four real-world social network datasets, and experimental results demonstrate that our model averagely outperforms baselines by 7.0% in accuracy.

Measuring Similarity Between Any Pair of Passengers Using Smart Card Usage Data

Recent years have witnessed considerable progress in the application of Internet of Things (IoT) technology in smart transportation systems. The wider presence of Wi-Fi networks in subway gates allows passengers to use the quick response (QR) code of mobile phone applications for entrance. The network established by gates has become a medium which connects stations and passengers. However, in addition to directly monitoring the passenger flow, the potential application of the smart card usage data collected by the gates remains an open topic. Although there are several clustering-based works devoted to revealing passengers’ travel behavior patterns, research on the social attributes of subway passengers is very limited. To fill the gap, this article proposes a novel method to mine similarity information of passengers by leveraging passengers’ communication behaviors hidden in subway card usage data. Passengers are first organized as a graph, which not only reflects the interactions between them but also incorporates the context information of subway stations. Then, the node embedding is used to encode the information contained in the graph and with the use of cosine similarity, the similarity between two passengers is measured. Extensive experiments on two real-world location-based social network data sets and extended experiments on a Shanghai subway data set are conducted. The results show that the proposed method can effectively improve the accuracy of similarity measurement and provide social features that are distinguishable from travel behavior patterns.

∊-k anonymization and adversarial training of graph neural networks for privacy preservation in social networks

With the explosive growth of social networks, privacy preservation as a social good has been one common concern. Graph neural networks (GNNs) have been utilized by social network service providers to improve business service. However, traditional anonymization techniques of social networks cannot satisfy the desired privacy preservation of node attribute and graph structure and introduce information disturbance from the anonymization, leading to the performance degradation of GNNs in social network analysis. To protect sensitive user data and persist GNNs’ performance in social network analysis, we propose a two-stage privacy-preserving method of graph neural networks in the social network domain. During the first stage, we design a novel ∊-k anonymization method that can achieve ∊-local differential privacy (∊-LDP) and k-degree anonymity by incorporating the classical LDP and k-degree anonymization (k-DA) while retaining as much network community information as possible. At the second stage, we develop an adversarial training mechanism for GNNs to resist the disturbance from ∊-k anonymization and retain as much task performance as possible on anonymous social network data. Comprehensive experiments on several real-world social network datasets demonstrate the effectiveness of the proposed method for privacy-preserving node classification, link prediction, and graph clustering in social networks. The proposed method represents an interesting and important combination of classical anonymous technologies and recent GNNs and can preserve user privacy while providing business service.

Multi-objective NSGA-II based community detection using dynamical evolution social network

Community detection is becoming a highly demanded topic in social networking-based applications. It involves finding the maximum intraconnected and minimum inter-connected sub-graphs in given social networks. Many approaches have been developed for community’s detection and less of them have focused on the dynamical aspect of the social network. The decision of the community has to consider the pattern of changes in the social network and to be smooth enough. This is to enable smooth operation for other community detection dependent application. Unlike dynamical community detection Algorithms, this article presents a non-dominated aware searching Algorithm designated as non-dominated sorting based community detection with dynamical awareness (NDS-CD-DA). The Algorithm uses a non-dominated sorting genetic algorithm NSGA-II with two objectives: modularity and normalized mutual information (NMI). Experimental results on synthetic networks and real-world social network datasets have been compared with classical genetic with a single objective and has been shown to provide superiority in terms of the domination as well as the convergence. NDS-CD-DA has accomplished a domination percentage of 100% over dynamic evolutionary community searching DECS for almost all iterations.

LTHS: A heuristic algorithm based on local two-hop search strategy for influence maximization in social networks

Influence maximization is a classic network optimization problem, which has been widely used in the field of viral marketing. The influence maximization problem aims to find a fixed number of active nodes. After a specific propagation model, the number of active nodes reaches the maximum. However, the existing influence maximization algorithms are overly pursuing certain indicators of efficiency or accuracy, which cannot be well accepted by some researchers. This paper proposes an effective algorithm to balance the accuracy and efficiency of the influence maximization problem called local two-hop search algorithm (LTHS). The core of the proposed algorithm is a node not only be affected by one-hop neighbor nodes, but also by two-hop neighbor nodes. Firstly, this paper selects initial seed nodes according to the characteristics of the node degree. Generally, the high degree of nodes regards as influential nodes. Secondly, this paper proposes a node two-hop influence evaluate function called two-hop diffusion value (THDV), which can evaluate node influence more accurately. Furthermore, in order to seek higher efficiency, this paper proposes a method to reduce the network scale. This paper conducted full experiments on five real-world social network datasets, and compared with other four well-known algorithms. The experimental results show that the LTHS algorithm is better than the comparison algorithms in terms of efficiency and accuracy.

Influence Maximization based on a Non-dominated Sorting Genetic Algorithm

Influence Maximization (IM) is a problem represented by a set of users who are specified in advance and are usually called the seed. The latter can influence their friends, who can in turn influence others and so on until it reaches the largest number of users within the network. This issue is of ultimate importance in a variety of fields. In the current study, a Non-dominated Sorting Genetic Algorithm II (NSGA-II) has been adopted in influence maximization to produce the so-called NSGAII based IM algorithm (NSGAII-IM). Principally, the population should be represented with individuals of variable lengths as the seed group, and the diffusion model should be designed so as to formulate its multi-objective function. In the context of individual representation, the nodes have been pseudo-randomly chosen using the centrality measures (based on high centrality nodes as degree, closeness, and eigenvector). As for the multi-objective function, increasing the coverage size of influence and decreasing the number of seed nodes as far as possible have been set as the conflicting objectives. Weighted Integration Cascade (WIC) has been suggested as an improved version of the Independent Cascade (IC) diffusion model. It has proven to be effective in the performance of the NSGAII-IM algorithm. In evaluating the proposed optimization model, two real-world social network datasets have been used: Facebook wall posts, and Digg networks. The algorithm showed promising results as it could relatively improve the solutions as compared with other methods, with an increased average of influential spread. Additionally, the WIC model has proven to be effective through the evaluation of the performance of the NSGAII-IM algorithm with other diffusion models.

An Attention-Based Graph Neural Network for Spam Bot Detection in Social Networks

With the rapid development of social networks, spam bots and other anomaly accounts’ malicious behavior has become a critical information security problem threatening the social network platform. In order to reduce this threat, the existing research mainly uses feature-based detection or propagation-based detection, and it applies machine learning or graph mining algorithms to identify anomaly accounts in social networks. However, with the development of technology, spam bots are becoming more advanced, and identifying bots is still an open challenge. This paper proposes a new semi-supervised graph embedding model based on a graph attention network for spam bot detection in social networks. This approach constructs a detection model by aggregating features and neighbor relationships, and learns a complex method to integrate the different neighborhood relationships between nodes to operate the directed social graph. The new model can identify spam bots by capturing user features and two different relationships among users in social networks. We compare our method with other methods on real-world social network datasets, and the experimental results show that our proposed model achieves a significant and consistent improvement.

Graph classification based on structural features of significant nodes and spatial convolutional neural networks

Many real-world problems can be abstracted into graph classification problems. Recently, graph convolutional networks have achieved great success in the task of node classification and link prediction. However, when using graph convolution network to process the task of graph classification, either global topology information or local information is ignored. Therefore, designing graph convolutional networks to improve the accuracy of graph classification has attracted more and more attention. Inspired by the use of convolutional neural networks to process graph-structured data, we put forward a new spatial convolutional neural network architecture for graph classification. To be specific, we first design a comprehensive weighting method to measure the significance of vertices in the graph based on multiple indicators to choose the central node sequence. Then, the normalization process of the graph is realized by constructing the same size neighborhood graphs for the central vertices. After that, the structural characteristics of the graph are extracted from both local and global aspects. Finally, the tensors obtained after the above steps are respectively input into the following two spatial convolutional neural network architectures to perform classification, one is a simple CNN structure, which has only two convolution layers, one dense layer and one softmax layer. The other is to modify the architecture of CNN, and the channel concatenation layer is introduced to determine the classification result of the entire graph according to the category of the neighborhood graphs. Experimental results on two kinds of real-world datasets, bioinformatics and social network datasets, indicate that our approach obtains competitive results and is superior to some classic kernels and similar deep learning-based algorithms on 6 out of 8 benchmark data sets.

Disclose More and Risk Less: Privacy Preserving Online Social Network Data Sharing

Many third-party services and applications have integrated the login services of popular Online Social Networks, such as Facebook and Google+, and acquired user information to enrich their services by requesting user's permission. Although users can control the information disclosed to the third parties in a certain granularity, there are still serious privacy risks due to the inference attack. Even if users conceal their sensitive information, attackers can infer their secrets by exploiting the correlations among private and public information with background knowledge. To defend against such attacks, we formulate the social network data sharing problem through an optimization-based approach, which maximizes the users’ self-disclosure utility while preserving their privacy. We propose two privacy-preserving social network data sharing methods to counter the inference attack. One is the efficiency-based privacy-preserving disclosure algorithm (EPPD) targeting the high utility, and the other is to convert the original problem into a multi-dimensional knapsack problem (d-KP) using greedy heuristics with a low computational complexity. We use real-world social network datasets to evaluate the performance. From the results, the proposed methods achieve a better performance when compared with the existing ones.

MFRep: Joint user and employer alignment across heterogeneous social networks

Nowadays people join multiple social networks simultaneously to enjoy a variety of social services. Apart from common users, social networks also share other information entities such as organizations/companies. Aligning common information entities across heterogeneous social networks is challenging, but will facilitate many applications. Existing approaches for network alignment do not fully leverage indirect relations among users (i.e., friends of friends). This leads to relatively poor performance, especially when there are little overlapping social relations between different networks. Meanwhile, the mutual promotion between two kinds of information entity alignment is often neglected.In this paper, we propose a novel Matrix Factorization based Representation learning (MFRep) framework. MFRep investigates the joint learning for user and employer alignment across different networks. (1). We first compute the cross-network similarities in user attributes to extract seed potential anchor users. Its efficiency can be boosted by considering the similarities in employer properties. (2). Considering social relation differences across networks, we construct user relational matrix to preserve multi-step relational information. This embodies the information propagation from known and seed potential anchor users to other potential anchor users. (3). We extend the user relational matrix to preserve consistent associations between users and employers across networks. (4). We perform semi-supervised user representation learning and unsupervised employer representation learning concurrently via efficient matrix decomposition. The correspondence between users/employers across networks can be inferred based on vector relevance. Extensive experiments on real-world social network datasets justify the utility of MFRep, even with dissimilar social structures across networks. MFRep illustrates the mutual promoted alignments of users and employers with a middle degree of scalability.