Recommendation systems have become ubiquitous tools in online platforms, providing personalized suggestions based on user–item interactions. To capture the dynamic higher-order connections between users and items, recommendation approaches based on dynamic graphs have garnered significant attention from researchers. However, existing recommendation methods based on dynamic graphs are often limited by data sparsity, which prevents them from achieving satisfactory performance. Fortunately, the rapid development of large language models (LLMs) with powerful text generation capabilities and extensive domain knowledge has offered new possibilities for addressing this challenge. However, how to effectively integrate LLMs with dynamic graphs remains unexplored. To bridge this gap, in this article, we propose a novel framework, that is, DynLLM, for applying LLMs to dynamic graph-based recommendation methods. Specifically, DynLLM harnesses the power of LLMs to generate multi-faceted user profiles based on the rich textual features of historical purchase records, which in turn supplement and enrich the underlying relationships between users and items. Along this line, to fuse the multi-faceted profiles with temporal graph embedding, we engage LLMs to derive corresponding profile embeddings and further employ a distilled attention mechanism to refine the LLM-generated profile embeddings for alleviating noisy signals, while also assessing and adjusting the relevance of each distilled facet embedding for seamless integration with temporal graph embedding from continuous time dynamic graphs (CTDGs). Extensive experiments on three real datasets have validated the superior improvements of DynLLM over a wide range of state-of-the-art baseline methods. The implementation code is available online at https://github.com/meteor-gif/DynLLM .

A well-developed recommendation system can not only leverage multi-typed interactions (such as page view , add-to-cart , and purchase ) to better identify user preferences but also demonstrate high performance, low complexity, and strong interpretability. However, many existing solutions for multi-behavior recommendation fall short of intuitive modeling of real-world scenarios, leading to overly complex models with massive parameters and cumbersome components. In particular, they share two critical limitations: (1) Some pioneering models are built upon the strict assumption of cascade effects across behaviors, which contradicts multifarious behavior paths in practical applications. (2) Existing approaches fail to explicitly capture the unique idiosyncrasies of users and even neglect the inherent nature of items involved in the multi-behavior interactions. To this end, we propose a novel Directed Acyclic Graph Convolutional Network (DA-GCN) for the multi-behavior recommendation task. Specifically, we pinpoint the partial order relations within the monotonic behavior chain and extend it to personalized directed acyclic behavior graphs to exploit behavior dependencies. Then, a GCN-based directed edge encoder is employed to distill rich collaborative signals embodied by each directed edge. In light of the information flows over the directed acyclic structure, we propose an attentive aggregation module to gather messages from all potential antecedent behaviors, representing distinct perspectives to understand the terminated behavior. Thus, we obtain comprehensive representations for the follow-up behavior through learnable distributions over its preceding behaviors, explicitly reflecting personalized interactive patterns of users and underlying properties of items simultaneously. Finally, we design a customized multi-task learning objective for flexible joint optimization. Extensive experiments on public benchmarking datasets fully demonstrate the superiority of DA-GCN with significant performance improvement and computational efficiency over a wide range of state-of-the-art methods. Our code is available at https://github.com/xizhu1022/DA-GCN .

Graph learning methods have achieved remarkable performance in various domains such as social recommendation, financial fraud detection, and so on. In real applications, the underlying graph is often dynamically evolving and thus, some recent studies focus on integrating the temporal topology information of graphs into the GNN for learning graph embedding. However, the robustness of training GNNs for dynamic graphs has not been discussed so far. The major reason is how to attack dynamic graph embedding still remains largely untouched, let alone how to defend against the attacks. To enable robust training of GNNs for dynamic graphs, in this paper, we investigate the problem of how to generate attacks and defend against attacks for dynamic graph embedding. Attacking dynamic graph embedding is more challenging than attacking static graph embedding as we need to understand the temporal dynamics of graphs as well as its impact on the embedding and the injected perturbations should be distinguished from the natural evolution. In addition, the defense is very challenging as the perturbations may be hidden within the natural evolution. To tackle these technical challenges, in this paper, we first develop a novel gradient-based attack method from an optimization perspective to generate perturbations to fool dynamic graph learning methods, where a key idea is to use gradient dynamics to attack the natural dynamics of the graph. Further, we borrow the idea of the attack method and integrate it with adversarial training to train a more robust dynamic graph learning method to defend against hand-crafted attacks. Finally, extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed attack and defense method, where our defense method not only achieves comparable performance on clean graphs but also significantly increases the defense performance on attacked graphs.

Dynamic share recommendation, which aims at recommending a friend who would like to share a particular item at a certain timestamp, has emerged as a novel task for social-oriented e-commerce platforms. Different from traditional graph-based recommendation tasks, with integrating the interconnected social interactions and fine-grained temporal information from historical share records, this novel task may encounter one unique challenge, i.e., how to deal with the dynamic social connections and asymmetric share interactions. Even worse, users may keep inactive during some periods, which results in difficulties in updating personalized profiles. To address the above challenges, in this paper, we propose a dynamic graph share recommendation model called DynShare. Specifically, we first divide each user embedding into two parts, namely the invitation embedding and vote embedding to show the tendencies of sending and receiving items, respectively. Then, temporal graph attention networks (TGATs) based on bi-directional continuous time dynamic graphs (CTDGs) are leveraged to encode temporal neighbor information from different directions. Afterward, to estimate how different users perceive the time intervals after the last interaction, we further design a time-interval aware personalized projection operator on the foundation of temporal point processes (TPPs) to project user embedding for the next-time share prediction. Extensive experiments on a real-world e-commerce share dataset have demonstrated that our proposed DynShare can achieve better results compared with state-of-the-art baseline methods. And our code is available on the project website: https://github.com/meteor-gif/DynShare.

Enterprise competition analysis has long been treated as a crucial task of management science, which can reveal pertinent information about market saturation and business opportunities to support the decision-making process of entrepreneurs and investors. Recently, with the development of graph representation techniques, enterprises could be now formulated in a novel graph-oriented perspective to model their cooperation and competition in a directional graph. However, these prior arts mainly treat the enterprises as individual nodes, while the ecosystems, i.e., enterprise groups formed based on their common interests, and consequent interaction effects have been largely ignored. To address this issue, in this article, by adapting the concept of hypergraph to describe enterprise ecosystems, we propose a novel multiview enterprise relation network (MERN) framework for enterprise competition analysis. Specifically, we first put forward a hypertranslating embedding (HTransE) algorithm inspired by translation distance models for the hyperrelation embedding of ecosystems. Meanwhile, a relational graph convolutional network (RGCN) is designed for ordinary relation embedding, e.g., supplier, client, or competitor. Afterward, considering the commercial diversity in ecosystems, which means that different enterprises may focus on different industry fields, we propose an industry trend embedding module to describe industry distribution and development trend factors for each enterprise. Finally, we apply the self-attention mechanism to integrate these three modules and adapt the bilinear model DistMult for competition link analysis. Extensive experiments on a real-world dataset validate that our solution can achieve better performance compared with several competitive baselines.

Abstractive summarization is a classic task in Natural Language Generation (NLG), which aims to produce a concise summary of the original document. Recently, great efforts have been made on sequence-to-sequence neural networks to generate abstractive sum- maries with a high level of fluency. However, prior arts mainly focus on the optimization of token-level likelihood, while the rich semantic information in documents has been largely ignored. In this way, the summarization results could be vulnerable to hallucinations, i.e., the semantic-level inconsistency between a summary and corresponding original document. To deal with this challenge, in this paper, we propose a novel fact-aware abstractive summarization model, named Entity-Relation Pointer Generator Network (ERPGN). Specially, we attempt to formalize the facts in original document as a factual knowledge graph, and then generate the high-quality summary via directly modeling consistency between summary and the factual knowledge graph. To that end, we first leverage two pointer net- work structures to capture the fact in original documents. Then, to enhance the traditional token-level likelihood loss, we design two extra semantic-level losses to measure the disagreement between a summary and facts from its original document. Extensive experi- ments on public datasets demonstrate that our ERPGN framework could outperform both classic abstractive summarization models and the state-of-the-art fact-aware baseline methods, with significant improvement in terms of faithfulness.