Network alignment, which integrates multiple network resources by identifying anchor nodes that exist in different networks, is beneficial for conducting comprehensive network analysis. Although there have been many studies on network alignment, most of them are limited to static scenarios and only can achieve acceptable top-$\alpha$($\alpha \gt 10$) results. In the absence of considering dynamic changes in networks, accurate network alignment (i.e., top-1 result) faces two problems: 1) Missing information: focusing solely on aligning networks at a specific time leads to low top-1 performance due to the lack of information from other time periods; 2) Confusing information: ignoring temporal information and focusing on aligning networks across the entire time span leads to low top-1 performance due to inability to distinguish the neighborhood nodes of anchor nodes. In this paper, we propose a dynamic network alignment method, which aims to achieve better top-1 alignment results with consider changing network structures over time. Towards this end, we learn the representations of nodes in the changing network structure with time, and preserve the consistency of anchor node pairs during the time-evolution process. Firstly, we employ a Structure-Time-aware module to capture network dynamics while preserving network structure and learning node representations that incorporate temporal information. Secondly, we ensure the global and local consistency of anchor node pairs over time by utilizing linear and similarity functions, respectively. Finally, we determine whether two nodes are anchor node pairs by maintaining consistency between global, local, and node representations. Experimental results obtained from real-world datasets demonstrate that the proposed model achieves performance comparable to several state-of-the-art methods.

The task of network alignment aims to identify corresponding nodes across multiple networks, with applications in various fields such as social network analysis and bioinformatics. Traditional methods typically focus on the topological structure of networks at a specific level, but they may overlook important properties exhibited by many networks, such as scale-free properties and specific power-law structures often found in social networks. Consequently, these methods fail to effectively capture and utilize such information, leading to misalignment. In this article, we propose a network alignment framework that incorporates both topological and attribute information from multiple levels in the network, including homogeneity, power-law, and higher order structures. We introduce a Euclidean hyperbolic interactive graph learning method specifically designed for modeling power-law structures in networks, aiming to improve the accuracy of network alignment. To evaluate the effectiveness of our proposed method, we conduct experiments on several real-world datasets. The results demonstrate that our approach achieves higher accuracy compared to other advanced baselines.

Network alignment is a fundamental problem in various domains since it can establish bridges for the same entity (i.e., anchor nodes) between different networks. Most existing network alignment methods are based on consistency assumption, i.e., anchor nodes exhibit similar local structures or neighbors across different networks. However, many anchor nodes have different local structures or neighbors across different networks, which could be regarded as anchor nodes' heterogeneity. It poses a challenge to methods based on the assumption of consistency, as they lack abundant shared information, such as common neighbors. Fortunately, network communities provide the comprehension of node relationships and group structures within networks, which could alleviate the information insufficient. In this article, we propose to address the challenge of inadequate shared information triggered by nodes' heterogeneity from a community perspective. Our model is based on joint optimization of node representation learning and community discovery, including: 1) a node-level constraint is employed to bring nodes with more anchor pairs as neighbors closer together and 2) a community-level constraint is utilized to bring nodes with higher order similarity closer together. We model the cross-network community alignment relations as asymmetric to mitigate the interference caused by anchor node heterogeneity when measuring community alignment relations. Furthermore, we leverage the learned cross-network community alignment relations to supplement node alignment, which could narrow down the search range of potential anchor nodes by focusing solely on aligning nodes within aligned cross-network communities. We conducted extensive experiments on real-world datasets, and the results show the effectiveness and efficiency of our proposed model on network alignment.

Finding experts is essential in Community Question Answering (CQA) platforms as it enables the effective routing of questions to potential users who can provide relevant answers. The key is to personalized learning expert representations based on their historical answered questions, and accurately matching them with target questions. Recently, the applications of Pre-Trained Language Models (PLMs) have gained significant attraction due to their impressive capability to comprehend textual data, and are widespread used across various domains. There have been some preliminary works exploring the usability of PLMs in expert finding, such as pre-training expert or question representations. However, these models usually learn pure text representations of experts from histories, disregarding personalized and fine-grained expert modeling. For alleviating this, we present a personalized pre-training and fine-tuning paradigm, which could effectively learn expert interest and expertise simultaneously. Specifically, in our pre-training framework, we integrate historical answered questions of one expert with one target question, and regard it as a candidate-aware expert-level input unit. Then, we fuse expert IDs into the pre-training for guiding the model to model personalized expert representations, which can help capture the unique characteristics and expertise of each individual expert. Additionally, in our pre-training task, we design (1) a question-level masked language model task to learn the relatedness between histories, enabling the modeling of question-level expert interest; (2) a vote-oriented task to capture question-level expert expertise by predicting the vote score the expert would receive. Through our pre-training framework and tasks, our approach could holistically learn expert representations including interests and expertise. Our method has been extensively evaluated on six real-world CQA datasets, and the experimental results consistently demonstrate the superiority of our approach over competitive baseline methods.

Network alignment involves identifying corresponding nodes across multiple networks. The majority of existing methods adhere to the assumption of consistency. However, due to distinct graph generation mechanisms, anchor nodes in real-world datasets often exhibit more intricate structural patterns, such as having multiple different neighbors and higher-order associations. Relying solely on consistency while disregarding the intricate patterns of anchor links may potentially inflict substantial detriment upon both the accuracy of network alignment and the generality of the model. In this paper, we introduce the disparity and diversity based on distinct structural patterns of ubiquitous anchor links. We propose a comprehensive framework that employs first-order proximity, lower-order discriminability, and higher-order correlation to model consistency, disparity, and diversity. We also incorporate a post-fusion mechanism for effectively integrating alignment matrices. Furthermore, we innovatively introduce hyperbolic space as an embedding space to further minimize embedding distortion. Extensive experiments have shown that our approach achieves state-of-the-art alignment results and yields notable improvements in the overall versatility of the model.

Network embedding is an important class of link prediction methods, which can use the distance between learned low-dimensional node representations to characterize the similarity between nodes. Traditional network embedding methods focus on single-layer networks, while in reality, a large part of complex networks are not isolated, but interdependent and interrelated, forming multiplex complex networks. Also, how to effectively exploit layer correlations in multiplex networks to learn more robust and valuable representations, to improve link prediction performance, has been a hot research topic in the field of complex network analysis. However, previous studies mainly focus on inferring intralinks in each layer of complex networks or anchor links among layers. Another issue that has not been discussed is how to predict potential links or reconstruct the network in unobserved relations based on existing multiplex networks. To this issue, we define a novel inductive link prediction problem in multiplex networks, in which most existing multichannel network embedding methods fail to solve. This is either because they only emphasize the specific structure information of an individual layer or only capture the common information for all layers. To effectively address this problem, we propose a novel embedding method termed interactive learning across relations (ILAR), to capture and fully exploit the multiple relations and complex layer correlations in multiplex networks. We leverage two convolutional modules and ILAR to capture the sufficient complementary and correlations in multiplex networks. Moreover, during interactive learning, a disparity constraint is introduced, which enforces the features encoded from two convolutional modules to be different and prevents information redundancy. Finally, the extensive experiments in several real-world datasets show that our model can significantly outperform the existing state-of-the-art network embedding methods on the novel link prediction problem in multiplex networks.

Temporal community detection is helpful to discover and analyze significant groups or clusters hidden in dynamic networks in the real world. A variety of methods, such as modularity optimization, spectral method, and statistical network model, has been developed from diversified perspectives. Recently, network embedding-based technologies have made significant progress, and one can exploit deep learning superiority to network tasks. Although some methods for static networks have shown promising results in boosting community detection by integrating community embedding, they are not suitable for temporal networks and unable to capture their dynamics. Furthermore, the dynamic embedding methods only model network varying without considering community structures. Hence, in this article, we propose a novel unsupervised dynamic community detection model, which is based on network embedding and can effectively discover temporal communities and model dynamic networks. More specifically, we propose the community prior by introducing the Gaussian mixture model (GMM) in the variational autoencoder, which can obtain community information and better model the evolutionary characteristics of community structure and node embedding by utilizing the variant of gated recurrent unit (GRU). Extensive experiments conducted in real-world and artificial networks demonstrate that our proposed model has a better effect on improving the accuracy of dynamic community detection.

Anomaly detection in dynamic networks aims to find network elements (e.g., nodes, edges, subgraphs, change points) with significantly different behaviors from the vast majority, it can also devote to community detection and evolution and prediction tasks. Most existing methods focus on one specific task, that is, only detect anomalies of one type of element isolated, so they lose the ability to model the correlation and driving mechanism between different abnormal behavior. Considering that the anomaly detection of one type of element is helpful to other types of elements, i.e., the temporal evolution hidden the dynamic networks are driven by indivisible behavior patterns. So in this paper, we propose a unified Generation model to analyze the dynamic network for Exploring the Abnormal Behaviors of different Scales (GEABS). It can model the relation and catch different levels (node, community and network) of anomaly with a joint statistical network model and detect the community structure and its evolution. Specifically, we denote the parameters of node popularity, community membership to generate the dynamic network with stochastic block model (SBM), we also describe the varying of node and community by dynamic process. With a well-designed generative mechanism, it can detect the change point on network level, temporal evolution on community level and abnormal behavior on node level synchronously, besides, it also detects the community structure effectively. We also propose an effective optimization algorithm with variational inference. Experimental results show that the GEABS achieves better performance on abnormal behavior and community structure compared with baselines.

In Community Question Answering (CQA) websites, expert finding aims at seeking suitable experts to answer questions. The key is to explore the inherent relevance based on the representations of questions and experts. Existing methods usually learn these features from single view information (e.g., question title), which would be not insufficient to fully learn their representations. In this paper, we propose a personalized expert finding method with a multi-view attentive matching mechanism. We design three modules under the multi-view paradigm, including a question encoder, an intra-view encoder, and an inter-view encoder, which aims to comprehend the comprehensive relationships between experts and questions. In the question encoder, we learn the multi-view question features from its title, body and tag views respectively. In the intra-view encoder, we design an interactive attention network to capture the view-specific relevance between the target question and the historical answered questions of experts for all different views. Furthermore, in the inter-view encoder we employ a personalized attention network to aggregate different view information to learn expert/question representations. In this way, the match of the expert and question could be fully captured from the multi-view information via the intra- and inter-view mechanisms. Experimental results on six datasets demonstrate that the proposed method could achieve better performance than existing state-of-the-art methods.

The dependencies across different layers are an important property in multiplex networks and a few methods have been proposed to learn the dependencies in various ways. When capturing the dependencies across different layers, some of them assumed the structure among layers following consistent connectivity to force two nodes with a link in one layer tend to have links in other layers, some introduced a common vector to model the shared information across all layers. However, the correlations among layers in multiplex networks are diverse, which go beyond the connectivity consistency. In this paper, we propose a novel Modeling Correlations for Multiplex network Embedding (MCME) framework to learn the robust node representations for each layer. It can deal with complex correlations with a common structure, layer similarity and node heterogeneity through a unified framework in multiplex networks. To evaluate our proposed model, we conduct extensive experiments on several real-world datasets and the results demonstrate that our proposed model consistently outperforms state-of-the-art methods.