Existing cross-network node classification methods are mainly proposed for closed-set setting, where the source network and the target network share exactly the same label space. Such a setting is restricted in real-world applications, since the target network might contain additional classes that are not present in the source. In this work, we study a more realistic open-set cross-network node classification (O-CNNC) problem, where the target network contains all the known classes in the source and further contains several target-private classes unseen in the source. Borrowing the concept from open-set domain adaptation, all target-private classes are defined as an additional “unknown” class. To address the challenging O-CNNC problem, we propose an unknown-excluded adversarial graph domain alignment (UAGA) model with a separate-adapt training strategy. Firstly, UAGA roughly separates known classes from unknown class, by training a graph neural network encoder and a neighborhood-aggregation node classifier in an adversarial framework. Then, unknown-excluded adversarial domain alignment is customized to align only target nodes from known classes with the source, while pushing target nodes from unknown class far away from the source, by assigning positive and negative domain adaptation coefficient to known class nodes and unknown class nodes. Extensive experiments on real-world datasets demonstrate significant outperformance of the proposed UAGA over state-of-the-art methods on O-CNNC.

Graph neural networks (GNNs) have shown great ability in modeling graphs; however, their performance would significantly degrade when there are noisy edges connecting nodes from different classes. To alleviate negative effect of noisy edges on neighborhood aggregation, some recent GNNs propose to predict the label agreement between node pairs within a single network. However, predicting the label agreement of edges across different networks has not been investigated yet. Our work makes the pioneering attempt to study a novel problem of cross-network homophilous and heterophilous edge classification (CNHHEC) and proposes a novel domain-adaptive graph attention-supervised network (DGASN) to effectively tackle the CNHHEC problem. First, DGASN adopts multihead graph attention network (GAT) as the GNN encoder, which jointly trains node embeddings and edge embeddings via the node classification and edge classification losses. As a result, label-discriminative embeddings can be obtained to distinguish homophilous edges from heterophilous edges. In addition, DGASN applies direct supervision on graph attention learning based on the observed edge labels from the source network, thus lowering the negative effects of heterophilous edges while enlarging the positive effects of homophilous edges during neighborhood aggregation. To facilitate knowledge transfer across networks, DGASN employs adversarial domain adaptation to mitigate domain divergence. Extensive experiments on real-world benchmark datasets demonstrate that the proposed DGASN achieves the state-of-the-art performance in CNHHEC.

Recent years, graph contrastive learning (GCL), which aims to learn representations from unlabeled graphs, has made great progress. However, the existing GCL methods mostly adopt human-designed graph augmentations, which are sensitive to various graph datasets. In addition, the contrastive losses originally developed in computer vision have been directly applied to graph data, where the neighboring nodes are regarded as negatives and consequently pushed far apart from the anchor. However, this is contradictory with the homophily assumption of net-works that connected nodes often belong to the same class and should be close to each other. In this work, we propose an end-to-end automatic GCL method, named NCLA to apply neighbor contrastive learning on learnable graph augmentation. Several graph augmented views with adaptive topology are automatically learned by the multi-head graph attention mechanism, which can be compatible with various graph datasets without prior domain knowledge. In addition, a neighbor contrastive loss is devised to allow multiple positives per anchor by taking network topology as the supervised signals. Both augmentations and embeddings are learned end-to-end in the proposed NCLA. Extensive experiments on the benchmark datasets demonstrate that NCLA yields the state-of-the-art node classification performance on self-supervised GCL and even exceeds the supervised ones, when the labels are extremely limited. Our code is released at https://github.com/shenxiaocam/NCLA.

This paper studies the problem of cross-network node classification to overcome the insufficiency of labeled data in a single network. It aims to leverage the label information in a partially labeled source network to assist node classification in a completely unlabeled or partially labeled target network. Existing methods for single network learning cannot solve this problem due to the domain shift across networks. Some multi-network learning methods heavily rely on the existence of cross-network connections, thus are inapplicable for this problem. To tackle this problem, we propose a novel graph transfer learning framework AdaGCN by leveraging the techniques of adversarial domain adaptation and graph convolution. It consists of two components: a semi-supervised learning component and an adversarial domain adaptation component. The former aims to learn class discriminative node representations with given label information of the source and target networks, while the latter contributes to mitigating the distribution divergence between the source and target domains to facilitate knowledge transfer. Extensive empirical evaluations on real-world datasets show that AdaGCN can successfully transfer class information with a low label rate on the source network and a substantial divergence between the source and target domains.

Network embedding is a highly effective method to learn low-dimensional node vector representations with original network structures being well preserved. However, existing network embedding algorithms are mostly developed for a single network, which fails to learn generalized feature representations across different networks. In this article, we study a cross-network node classification problem, which aims at leveraging the abundant labeled information from a source network to help classify the unlabeled nodes in a target network. To succeed in such a task, transferable features should be learned for nodes across different networks. To this end, a novel cross-network deep network embedding (CDNE) model is proposed to incorporate domain adaptation into deep network embedding in order to learn label-discriminative and network-invariant node vector representations. On the one hand, CDNE leverages network structures to capture the proximities between nodes within a network, by mapping more strongly connected nodes to have more similar latent vector representations. On the other hand, node attributes and labels are leveraged to capture the proximities between nodes across different networks by making the same labeled nodes across networks have aligned latent vector representations. Extensive experiments have been conducted, demonstrating that the proposed CDNE model significantly outperforms the state-of-the-art network embedding algorithms in cross-network node classification.

In this paper, the task of cross-network node classification, which leverages the abundant labeled nodes from a source network to help classify unlabeled nodes in a target network, is studied. The existing domain adaptation algorithms generally fail to model the network structural information, and the current network embedding models mainly focus on single-network applications. Thus, both of them cannot be directly applied to solve the cross-network node classification problem. This motivates us to propose an adversarial cross-network deep network embedding (ACDNE) model to integrate adversarial domain adaptation with deep network embedding so as to learn network-invariant node representations that can also well preserve the network structural information. In ACDNE, the deep network embedding module utilizes two feature extractors to jointly preserve attributed affinity and topological proximities between nodes. In addition, a node classifier is incorporated to make node representations label-discriminative. Moreover, an adversarial domain adaptation technique is employed to make node representations network-invariant. Extensive experimental results demonstrate that the proposed ACDNE model achieves the state-of-the-art performance in cross-network node classification.

Network embedding has attracted an increasing attention over the past few years. As an effective approach to solve graph mining problems, network embedding aims to learn a low-dimensional feature vector representation for each node of a given network. The vast majority of existing network embedding algorithms, however, are only designed for unsigned networks, and the signed networks containing both positive and negative links, have pretty distinct properties from the unsigned counterpart. In this paper, we propose a deep network embedding model to learn the low-dimensional node vector representations with structural balance preservation for the signed networks. The model employs a semisupervised stacked auto-encoder to reconstruct the adjacency connections of a given signed network. As the adjacency connections are overwhelmingly positive in the real-world signed networks, we impose a larger penalty to make the auto-encoder focus more on reconstructing the scarce negative links than the abundant positive links. In addition, to preserve the structural balance property of signed networks, we design the pairwise constraints to make the positively connected nodes much closer than the negatively connected nodes in the embedding space. Based on the network representations learned by the proposed model, we conduct link sign prediction and community detection in signed networks. Extensive experimental results in real-world datasets demonstrate the superiority of the proposed model over the state-of-the-art network embedding algorithms for graph representation learning in signed networks.

To maximize the influence across multiple heterogeneous networks, we propose an innovative cross-network learning model to study the influence maximization problem from two perspectives, namely, seed selection and graph sparsification. On one hand, we consider seed selection as a cross-network node prediction task, by leveraging the greedy seed selection knowledge prelearned in a smaller source network, to heuristically select the nodes most likely to act as seed for the target networks. On the other hand, we consider graph sparsification as a cross-network edge prediction problem, by adapting the influence propagation knowledge previously acquired in the source network to remove the edges least likely to contribute to influence propagation in the target networks. To address domain discrepancy, a fuzzy self-learning algorithm is proposed to iteratively train the prediction model by leveraging not only the fully labeled data in the source network, but also the most confident predicted instances with their predicted fuzzy labels in the target network. With such fuzzy labels, we can differentiate the confident levels of predictions generated by different self-training iterations, thus lowering the negative effects caused by less confident predictions. The performance of the proposed model is benchmarked with the popular influence maximization algorithms for seed selection; and also competed with several graph sparsification algorithms for inactive edge prediction. Experimental results on the real-world datasets show that the proposed cross-network learning model can achieve a good tradeoff between the efficiency and effectiveness of the influence maximization task in the target networks.

Network Embedding is the task of learning continuous node representations for networks, which has been shown effective in a variety of tasks such as link prediction and node classification. Most of existing works aim to preserve different network structures and properties in low-dimensional embedding vectors, while neglecting the existence of noisy information in many real-world networks and the overfitting issue in the embedding learning process. Most recently, generative adversarial networks (GANs) based regularization methods are exploited to regularize embedding learning process, which can encourage a global smoothness of embedding vectors. These methods have very complicated architecture and suffer from the well-recognized non-convergence problem of GANs. In this paper, we aim to introduce a more succinct and effective local regularization method, namely adversarial training, to network embedding so as to achieve model robustness and better generalization performance. Firstly, the adversarial training method is applied by defining adversarial perturbations in the embedding space with an adaptive L2 norm constraint that depends on the connectivity pattern of node pairs. Though effective as a regularizer, it suffers from the interpretability issue which may hinder its application in certain real-world scenarios. To improve this strategy, we further propose an interpretable adversarial training method by enforcing the reconstruction of the adversarial examples in the discrete graph domain. These two regularization methods can be applied to many existing embedding models, and we take DeepWalk as the base model for illustration in the paper. Empirical evaluations in both link prediction and node classification demonstrate the effectiveness of the proposed methods.

Domain adaptation refers to the learning scenario where a model learned from the source data is applied on the target data which have the same categories but different distributions. In information retrieval, there exist application scenarios like cross domain recommendation characterized similarly. In this paper, by utilizing deep features extracted from the deep networks, we proposed to compute the multi-layer joint kernelized mean distance between the k th target data predicted as the i th category and all the source data of the j th category $d_ij ^k$. Then, target data $T_m$ that are most likely to belong to the i th category can be found by calculating the relative distance $d_ii ^k/\sum_j d_ij ^k$. By iteratively adding $T_m$ to the training data, the finetuned deep model can adapt on the target data progressively. Our results demonstrate that the proposed method can achieve a better performance compared to a number of state-of-the-art methods.

Network embedding is an effective method to learn a low-dimensional feature vector representation for each node of a given network. In this paper, we propose a deep network embedding model with aggregated proximity preserving (DNE-APP). Firstly, an overall network proximity matrix is generated to capture both local and global network structural information, by aggregating different k-th order network proximities between different nodes. Then, a semi-supervised stacked auto-encoder is employed to learn the hidden representations which can best preserve the aggregated proximity in the original network, and also map the node pairs with higher proximity closer to each other in the embedding space. With the hidden representations learned by DNE-APP, we apply vector-based machine learning techniques to conduct node classification and link label prediction tasks on the real-world datasets. Experimental results demonstrate the superiority of our proposed DNE-APP model over the state-of-the-art network embedding algorithms.

When tackling large-scale influence maximization (IM) problem, one effective strategy is to employ graph sparsification as a pre-processing step, by removing a fraction of edges to make original networks become more concise and tractable for the task. In this work, a Cross-Network Graph Sparsification (CNGS) model is proposed to leverage the influence backbone knowledge pre-detected in a source network to predict and remove the edges least likely to contribute to the influence propagation in the target networks. Experimental results demonstrate that conducting graph sparsification by the proposed CNGS model can obtain a good trade-off between efficiency and effectiveness of IM, i.e., existing IM greedy algorithms can run more efficiently, while the loss of influence spread can be made as small as possible in the sparse target networks.

It is now a fact as well as a trend that people are increasingly relying on online social networks to work, study, and share with others. Thus, it is unavoidable for us to be influenced by others through online social networking. Studying social influence and information diffusion in online social networks can be remarkably useful in various real-life applications, notably influencer marketing and viral marketing. The Topical Affinity Propagation (TAP) model has been demonstrated with success to analyze the social influence on topic level. It manages to identify the most influential nodes on a given topic in social networks successfully. While TAP mainly focuses on the topic factor, it considers little about the complex relationship between individuals, which is crucial in calculating the influence probabilities between nodes. In this paper, the idea of quantitatively estimating the peer influence probability from a social persuasion perspective in sociology is exploited and consequently a persuasion-driven social influence analysis model is presented. Based on the TAP model and the social persuasion influence propagation measures, both the topical information and the persuasion influences between individuals are taken into consideration such that a social persuasion-driven influence analysis model is proposed. The experimental results on different data sets show that the social influences on a given topic can be discovered effectively by the proposed approach, especially under consideration of authority, the accuracy in identifying the most influential nodes can be significantly improved as compared with an existing work.

Nowadays with the readily accessibility of online social networks (OSNs), people are facilitated to share interesting information with friends through OSNs. Undoubtedly these sharing activities make our life more fantastic. However, meanwhile one challenge we have to face is information overload that we do not have enough time to review all of the content broadcasted through OSNs. So we need to have a mechanism to help users recognize interesting items from a large pool of content. In this project, we aim at filtering unwanted content based on the strength of trust relationships between users. We have proposed two kinds of trust models-basic trust model and source-level trust model. The trust values are estimated based on historical user interactions and profile similarity. We estimate dynamic trusts and analyze the evolution of trust relationships over dates. We also incorporate the auxiliary causes of interactions to moderate the noisy effect of user's intrinsic tendency to perform a certain type of interaction. In addition, since the trustworthiness of diverse information sources are rather distinct, we further estimate trust values at source-level. Our recommender systems utilize several types of Collaborative Filtering (CF) approaches, including conventional CF (namely user-based, item-based, singular value decomposition (SVD)based), and also trust-combined user-based CF. We evaluate our trust models and recommender systems on Friendfeed datasets. By comparing the evaluation results, we found that the recommendations based on estimated trust relationships were better than conventional CF recommendations.