The functional brain network (FBN) serves as an important tool for investigating neurological and mental disorders. Unlike many traditional networks whose structures are often known in advance, FBNs need to be estimated from neuroimaging or electrophysiological data, and their quality generally determines the performance of downstream tasks, particularly in disorder detection. Recent studies have shown that high-order FBNs tend to achieve better discriminative performance, while some other work indicates that increasing the order of FBN does not necessarily bring additional gains in discriminative performance and may even lead to a rapid decline in discriminability. To fully leverage information across different-order FBNs and identify optimal order for downstream tasks, we design an adaptive high-order FBN fusion learning framework (AHFL) with attention mechanism for brain disorder detection. Specifically, we first construct a series of FBNs with continuously increasing orders and propose a data-driven approach to evaluate each order's contribution to the classification performance. The self-attention mechanism is employed to capture contextual dependencies during the sequential generation of multi-order FBNs, thereby offering a natural fusion approach. Experimental evidence shows that the proposed method achieves superior performance compared to the baseline. In particular, we find that third-order FBN is achieved the highest weights, playing a crucial role for the detection of autism spectrum disorder (ASD), whereas second-order FBN is the most effective for identifying patients with major depressive disorder (MDD). Our findings advance the identification of discriminative high-order FBNs and establish a generalizable diagnostic framework for brain disorders.

With the explosive growth of scholarly information, researchers spend much time and effort copiously quoting authoritative works to support their ideas or motivations. We aim to alleviate this situation by proposing a citation recommendation strategy that recalls related papers for a rough idea (a piece of text, i.e., abstract, manuscript). However, the perspective of existing citation recommendations can not be well applied to our task for two defects. First, these methods neglect the reasoning of research topics, which makes the recommendation mechanism not meticulous enough and lacks explainability. For instance, they are not able to mine the hidden citing logic for the candidate paper while recommending. We fill the research gap by constructing structural topics consisting of knowledge concepts from the textual content, where reasoning paths between topics are extracted from an external knowledge graph. Second, the citation network is viewed as a crucial structural context to enhance the recommendation performance, but the new target idea does not have links to the citation network as published papers do. To simulate the prospective topological structure, our model, meanwhile, incorporates a contrastive-learning-based alignment paradigm to encourage the consistency of content embeddings and structure-oriented embeddings. We evaluate our proposed model on three real-world datasets and demonstrate that it significantly improves recommendation accuracy while providing high-quality knowledge-aware reasoning. And an interesting visual example illustrates the reasoning process when our model actually judges samples, which supports the feasibility of our topic-view learning paradigm.

Recommendation systems have attracted attention from academia and industry due to their wide range of application scenarios. However, cold start remains a challenging problem limited by sparse user interactions. Some scholars propose to transfer the dense information from the source domain to the target domain through cross-domain recommendation, but most of the work assumes that there is a small amount of historical interaction in the target domain. However, this approach essentially presupposes the existence of at least some historical interaction within the target domain. In this paper, we focus on the domain-level zero-shot recommendation (DZSR) problem. To address the above challenges, we propose a knowledge-aware cross-semantic alignment (K-CSA) framework to learn transferable source domain semantic information. The motivation is to establish stable alignments of interests in different domains through class semantic descriptions (CSDs). Specifically, due to the lack of effective information in the target domain, we learn semantic representations of source and target domain items based on knowledge graphs. Moreover, we conduct multi-view K-means to extract item CSDs from the learned semantic representations. Further, K-CSA learns universal user CSDs through the designed multi-head self-attention. To facilitate the transference of user interest from the source domain to the target domain, we devise a cross-semantic contrastive learning strategy, grounded in the prototype distribution matrix. We conduct extensive experiments on several real-world cross-domain datasets, and the experimental results clearly demonstrate the superiority of our proposed K-CSA compared with other baselines.

Influence maximization (IM) is formulated as selecting a set of initial users from a social network to maximize the expected number of influenced users. Researchers have made great progresses to design various traditional methods, yet both theoretical design and performance gain are close to their limits. In the past few years, learning-based IM methods have emerged to achieve stronger generalization ability to unknown graphs than traditional ones. However, the development of learning-based IM methods is still limited by fundamental obstacles, including 1) the difficulty of effectively solving the objective function; 2) the difficulty of characterizing the diversified and underlying diffusion patterns; and 3) the difficulty of adapting the solution under various node-centrality-constrained IM variants. To cope with the above challenges, we design a novel framework DeepIM to generatively characterize the latent representation of seed sets, and we propose to learn the diversified information diffusion pattern in a data-driven and end-to-end manner. Finally, we design a novel objective function to infer optimal seed sets under flexible node-centrality-based budget constraints. Extensive analyses are conducted over both synthetic and real-world datasets to demonstrate the overall performance of DeepIM.

Recently, Zero-Shot Node Classification (ZNC) has been an emerging and crucial task in graph data analysis. This task aims to predict nodes from unseen classes which are unobserved in the training process. Existing work mainly utilizes Graph Neural Networks (GNNs) to associate features' prototypes and labels' semantics thus enabling knowledge transfer from seen to unseen classes. However, the multi-faceted semantic orientation in the feature-semantic alignment has been neglected by previous work, i.e. the content of a node usually covers diverse topics that are relevant to the semantics of multiple labels. It's necessary to separate and judge the semantic factors that tremendously affect the cognitive ability to improve the generality of models. To this end, we propose a Knowledge-Aware Multi-Faceted framework (KMF) that enhances the richness of label semantics via the extracted KG (Knowledge Graph)-based topics. And then the content of each node is reconstructed to a topic-level representation that offers multi-faceted and fine-grained semantic relevancy to different labels. Due to the particularity of the graph's instance (i.e., node) representation, a novel geometric constraint is developed to alleviate the problem of prototype drift caused by node information aggregation. Finally, we conduct extensive experiments on several public graph datasets and design an application of zero-shot cross-domain recommendation. The quantitative results demonstrate both the effectiveness and generalization of KMF with the comparison of state-of-the-art baselines.

Analogical reasoning is the process of discovering and mapping correspondences from a target subject to a base subject. As the most well-known computational method of analogical reasoning, Structure-Mapping Theory (SMT) abstracts both target and base subjects into relational graphs and forms the cognitive process of analogical reasoning by finding a corresponding subgraph (i.e., correspondence) in the target graph that is aligned with the base graph. However, incorporating deep learning for SMT is still under-explored due to several obstacles: 1) the combinatorial complexity of searching for the correspondence in the target graph; 2) the correspondence mining is restricted by various cognitive theory-driven constraints. To address both challenges, we propose a novel framework for Analogical Reasoning (DeepGAR) that identifies the correspondence between source and target domains by assuring cognitive theory-driven constraints. Specifically, we design a geometric constraint embedding space to induce subgraph relation from node embeddings for efficient subgraph search. Furthermore, we develop novel learning and optimization strategies that could end-to-end identify correspondences that are strictly consistent with constraints driven by the cognitive theory. Extensive experiments are conducted on synthetic and real-world datasets to demonstrate the effectiveness of the proposed DeepGAR over existing methods. The code and data are available at: https://github.com/triplej0079/DeepGAR.

Graph diffusion problems such as the propagation of rumors, computer viruses, or smart grid failures are ubiquitous and societal. Hence it is usually crucial to identify diffusion sources according to the current graph diffusion observations. Despite its tremendous necessity and significance in practice, source localization, as the inverse problem of graph diffusion, is extremely challenging as it is ill-posed: different sources may lead to the same graph diffusion patterns. Different from most traditional source localization methods, this paper focuses on a probabilistic manner to account for the uncertainty of different candidate sources. Such endeavors require to overcome significant challenges along the way including: 1) the uncertainty in graph diffusion source localization is hard to be quantified; 2) the complex patterns of the graph diffusion sources are difficult to be probabilistically characterized; 3) the generalization under any underlying diffusion patterns is hard to be imposed. To solve the above challenges, this paper presents a generic framework: Source Localization Variational AutoEncoder (SL-VAE) for locating the diffusion sources under arbitrary diffusion patterns. Particularly, we propose a probabilistic model that leverages the forward diffusion estimation model along with deep generative models to approximate the diffusion source distribution for quantifying the uncertainty. SL-VAE further utilizes prior knowledge of the source-observation pairs to characterize the complex patterns of diffusion sources by a learned generative prior. Lastly, a unified objective that integrates the forward diffusion estimation model is derived to enforce the model to generalize under arbitrary diffusion patterns. Extensive experiments are conducted on $7$ real-world datasets to demonstrate the superiority of SL-VAE in reconstructing the diffusion sources by excelling the state-of-the-arts on average 20% in AUC score. The code and data are available at: https://github.com/triplej0079/SLVAE.

Localizing the source of graph diffusion phenomena, such as misinformation propagation, is an important yet extremely challenging task in the real world. Existing source localization models typically are heavily dependent on the hand-crafted rules and only tailored for certain domain-specific applications. Unfortunately, a large portion of the graph diffusion process for many applications is still unknown to human beings so it is important to have expressive models for learning such underlying rules automatically. Recently, there is a surge of research body on expressive models such as Graph Neural Networks (GNNs) for automatically learning the underlying graph diffusion. However, source localization is instead the inverse of graph diffusion, which is a typical inverse problem in graphs that is well-known to be ill-posed because there can be multiple solutions and hence different from the traditional (semi-)supervised learning settings. This paper aims to establish a generic framework of invertible graph diffusion models for source localization on graphs, namely Invertible Validity-aware Graph Diffusion (IVGD), to handle major challenges including 1) Difficulty to leverage knowledge in graph diffusion models for modeling their inverse processes in an end-to-end fashion, 2) Difficulty to ensure the validity of the inferred sources, and 3) Efficiency and scalability in source inference. Specifically, first, to inversely infer sources of graph diffusion, we propose a graph residual scenario to make existing graph diffusion models invertible with theoretical guarantees; second, we develop a novel error compensation mechanism that learns to offset the errors of the inferred sources. Finally, to ensure the validity of the inferred sources, a new set of validity-aware layers have been devised to project inferred sources to feasible regions by flexibly encoding constraints with unrolled optimization techniques. A linearization technique is proposed to strengthen the efficiency of our proposed layers. The convergence of the proposed IVGD is proven theoretically. Extensive experiments on nine real-world datasets demonstrate that our proposed IVGD outperforms state-of-the-art comparison methods significantly. We have released our code at https://github.com/xianggebenben/IVGD.