Knowledge tracing aims to dynamically track and assess learners’ mastery of specific knowledge concepts (e.g., item taxonomies, skill hierarchies). Integrating it into recommender systems greatly enhances model explainability, yet current models either overemphasize temporal dynamics or overlook inter-concept spatial correlations, resulting in suboptimal performance in integrated spatiotemporal modeling. Such disjointed designs also lead to limited adaptability in long learning sequences, poor compatibility with real-world dynamic scenarios, and restricted model interpretability. To solve these problems, we propose an A-SGNN framework fusing higher-order path spiking and graph neural networks, which leverages Graph Convolutional Network (GCN) and Spiking Neural Network (SNN) modules to make up for existing shortcomings. Specifically, a Bidirectional GCN module fully captures bidirectional high-order spatial graph structures between concepts, accurately modeling multi-knowledge relationships and reducing information loss. An SNN module with adaptive path-finding strategy dynamically optimizes individual learning trajectories, overcoming traditional fixed-path rigidity, while its membrane potential decay simulates human forgetting. A time decay factor is integrated to better capture memory effects in learning. Experiments demonstrate that our method outperforms state-of-the-art approaches in predicting learners’ future academic performance, with this advantage deriving from the combined synergistic benefits of the GCN and SNN modules. Our implementation is publicly available at: https://github.com/jianruichen/A-SGNN .

A novel dynamic network model is proposed to capture evolving latent communities within temporal networks. To achieve this, we decompose each observed dynamic edge between vertices using a Poisson-gamma edge partition model, assigning each vertex to one or more latent communities through nonnegative vertex-community memberships. Specifically, hierarchical transition kernels are employed to model the interactions between these latent communities. A hierarchical graph prior is placed on the transition structure of the latent communities, allowing us to model how they evolve and interact over time. Consequently, our dynamic network model enables the inferred community structure to merge, split, and interact with one another, providing a comprehensive understanding of complex network dynamics. Experiments on various real-world network datasets demonstrate that the proposed model not only effectively uncovers interpretable latent structures but also surpasses other state-of-the-art dynamic network models in the tasks of link prediction and community detection.

Network connectivity minimization is a fundamental problem in controlling the spread of viruses in the Internet and facilitating information propagation in online social networks. The problem aims to identify a budget number of key nodes whose removal would minimize the connectivity of a network. However, the existing solutions heavily rely on the number of edges, making it challenging to handle large and densely connected social networks. In this study, we present a fast algorithm that is independent of the number of edges. To achieve this, we first introduce a surrogate matrix that approximates the residual adjacency matrix with arbitrary small predefined error. We then devise an efficient approach for inferring k influential nodes by optimizing the eigenvalues of the surrogate matrix. Remarkably, the algorithm has a small time complexity of O(knr3), with r being a small tunable number. Our algorithm thereby maintains a linear scalability in terms of the number of nodes and is unaffected by the number of edges. Hence, it has the capability to efficiently handle large and dense social networks. At last, we evaluate its performance against state-of-the-art techniques using diverse real-world datasets. The experimental results demonstrate the superiority of our proposed method in terms of both solution quality and computational efficiency.

Large language models (LLMs) provide powerful foundations to perform fine-grained text re-ranking. However, they are often prohibitive in reality due to constraints on computation bandwidth. In this work, we propose a flexible architecture called Matroyshka Re-Ranker, which is designed to facilitate runtime customization of model layers and sequence lengths at each layer based on users' configurations. Consequently, the LLM-based re-rankers can be made applicable across various real-world situations. The increased flexibility may come at the cost of precision loss. To address this problem, we introduce a suite of techniques to optimize the performance. First, we propose cascaded self-distillation, where each sub-architecture learns to preserve a precise re-ranking performance from its super components, whose predictions can be exploited as smooth and informative teacher signals. Second, we design a factorized compensation mechanism, where two collaborative LoRA modules, vertical and horizontal, are jointly employed to compensate for the precision loss resulted from arbitrary combinations of layer and sequence compression. We perform comprehensive experiments using passage and document retrieval datasets from MSMARCO, along with all public datasets from BEIR. In our experiments, Matryoshka Re-Ranker substantially outperforms existing methods, while effectively preserving its superior performance across various compression forms and application scenarios. We have publicly released our method at this https://github.com/FlagOpen/FlagEmbedding repo.

Explainable recommender systems ( ERS ) aim to enhance users’ trust in the systems by offering personalized recommendations with transparent explanations. This transparency provides users with a clear understanding of the rationale behind the recommendations, fostering a sense of confidence and reliability in the system’s outputs. Generally, the explanations are presented in a familiar and intuitive way, which is in the form of natural language, thus enhancing their accessibility to users. Recently, there has been an increasing focus on leveraging reviews as a valuable source of rich information in both modeling user-item preferences and generating textual interpretations, which can be performed simultaneously in a multi-task framework. Despite the progress made in these review-based recommendation systems, the integration of implicit feedback derived from user-item interactions and user-written text reviews has yet to be fully explored. To fill this gap, we propose a model named SERMON (A s pect-enhanced E xplainable R ecommendation with M ulti-modal C o ntrast Lear n ing). Our model explores the application of multimodal contrastive learning to facilitate reciprocal learning across two modalities, thereby enhancing the modeling of user preferences. Moreover, our model incorporates the aspect information extracted from the review, which provides two significant enhancements to our tasks. Firstly, the quality of the generated explanations is improved by incorporating the aspect characteristics into the explanations generated by a pre-trained model with controlled textual generation ability. Secondly, the commonly used user-item interactions are transformed into user-item-aspect interactions, which we refer to as interaction triple, resulting in a more nuanced representation of user preference. To validate the effectiveness of our model, we conduct extensive experiments on three real-world datasets. The experimental results show that our model outperforms state-of-the-art baselines, with a 2.0% improvement in prediction accuracy and a substantial 24.5% enhancement in explanation quality for the TripAdvisor dataset.

Federated learning enables collaborative AI training across organizations without compromising data privacy. Decentralized federated learning (DFL) improves this by offering enhanced reliability and security through peer-to-peer (P2P) model sharing. However, DFL faces challenges in terms of slow convergence rate due to complex P2P graphs. To address this issue, we propose an efficient algorithm to accelerate DFL by introducing a limited number of k of edges into the P2P graphs. Specifically, we establish a connection between the convergence rate and the second smallest eigenvalue of the laplacian matrix of the P2P graph. We prove that finding the optimal set of edges to maximize this eigenvalue is an NP-complete problem. Our quantitative analysis shows the positive effect of strategic edge additions on improving this eigenvalue. Based on the analysis, we then propose an efficient algorithm to compute the best set of candidate edges to maximize the second smallest eigenvalue, and consequently the convergence rate is maximized. Our algorithm has a low time complexity of O(krn^2). Experimental results on diverse datasets validate the effectiveness of our proposed algorithms in accelerating DFL convergence.

Recommendation systems guide users in locating their desired information within extensive content repositories. Usually, a recommendation model is optimized to enhance accuracy metrics from a user utility standpoint, such as click-through rate or matching relevance. However, a responsible industrial recommendation model must address not only user utility (responsibility to users) but also other objectives, including increasing platform revenue (responsibility to platforms), ensuring fairness (responsibility to content creators), and maintaining unbiasedness (responsibility to long-term healthy development). Multi-objective learning is a promising approach for achieving responsible recommendation models. Nevertheless, current methods encounter two challenges: difficulty in scaling to heterogeneous objectives within a unified framework, and inadequate controllability over objective priority during optimization, leading to uncontrollable solutions.

The ease of spreading false information online enables individuals with malicious intent to manipulate public opinion and destabilize social stability. Recently, fake news detection based on evidence retrieval has gained popularity in an effort to identify fake news reliably and reduce its impact. Evidence retrieval-based methods can improve the reliability of fake news detection by computing the textual consistency between the evidence and the claim in the news. In this paper, we propose a framework for fake news detection based on MUlti- Step Evidence Retrieval enhancement (MUSER), which simulates the steps of human beings in the process of reading news, summarizing, consulting materials, and inferring whether the news is true or fake. Our model can explicitly model dependencies among multiple pieces of evidence, and perform multi-step associations for the evidence required for news verification through multi-step retrieval. In addition, our model is able to automatically collect existing evidence through paragraph retrieval and key evidence selection, which can save the tedious process of manual evidence collection. We conducted extensive experiments on real-world datasets in different languages, and the results demonstrate that our proposed model outperforms state-of-the-art baseline methods for detecting fake news by at least 3% in F1-Macro and 4% in F1-Micro. Furthermore, it provides interpretable evidence for end users.

In this paper, we present an algorithmic study on how to surpass competitors in popularity by strategic promotions in social networks. We first propose a novel model, in which we integrate the Preferential Attachment (PA) model for popularity growth with the Independent Cascade (IC) model for influence propagation in social networks called PA-IC model. In PA-IC, a popular item and a novice item grab shares of popularity from the natural popularity growth via the PA model, while the novice item tries to gain extra popularity via influence cascade in a social network. The popularity ratio is defined as the ratio of the popularity measure between the novice item and the popular item. We formulate Popularity Ratio Maximization (PRM) as the problem of selecting seeds in multiple rounds to maximize the popularity ratio in the end. We analyze the popularity ratio and show that it is monotone but not submodular. To provide an effective solution, we devise a surrogate objective function and show that empirically it is very close to the original objective function while theoretically, it is monotone and submodular. We design two efficient algorithms, one for the overlapping influence and non-overlapping seeds (across rounds) setting and the other for the non-overlapping influence and overlapping seed setting, and further discuss how to deal with other models and problem variants. Our empirical evaluation further demonstrates that our proposed method consistently achieves the best popularity promotion compared to other methods. Our theoretical and empirical analyses shed light on the interplay between influence maximization and preferential attachment in social networks.

Networks are one of the most powerful structures for modeling problems in the real world. Many machine learning algorithms, however, require that each input example is a real vector. Network embedding learns from feature representations of nodes and links in a network, and converts it to vectors. Community structure is an important feature of the network, which represents the relationship among nodes and attracts the attention of relevant researchers. Many algorithms have been developed to identify the community structure. These algorithms usually identify different communities in the network, generating different types of information. In this paper, we propose a "Community Splitter" model based on random walk and RNN (Recurrent Neural Networks) that combines the node information generated by multiple community detection algorithms to improve node representation and link prediction. Extensive experiments on nine real datasets demonstrate that our proposed Community Splitter model has a significant prediction power compared to state-of-the-art link prediction models.