Text-Attributed Graph (TAG) is an important type of graph structured data, where each node has a text description. TAG models usually train a Graph Neural Network (GNN) and language model jointly, which leads to high space and time consumption, especially on large datasets. To mitigate this, we propose TAGSAM, a condensation method that compresses TAGs while preserving training accuracy. TAGSAM comes with two key designs, i.e., subgraph text Selection and Attribute similarity Matching, which compress the text description and graph topology of TAG, respectively. For the texts, subgraph text selection selects and merges representative text chunks from multiple related text descriptions by maximizing mutual information. For the graph topology, popular condensation methods based on Matching Training Trajectories (MTT) suffer from high variance, which hinders accuracy. Our attribute similarity matching mitigates this issue by aligning stable similarity matrices. We evaluate TAGSAM against six state-of-the-art baselines, where it showcases superior performance. For the same compressed size, TAGSAM improves upon the best-performing baseline by an average of 4.9% in accuracy. Furthermore, it maintains competitive training accuracy even when the TAG is condensed to just 1% size. Our code is available at https://github.com/SundayVHan/TAGSAM

Answering complex queries over incomplete knowledge graphs (KGs) is a challenging job. Most previous works have focused on learning entity/relation embeddings and simulating first-order logic operators with various neural networks. However, they are bottlenecked by the inability to share world knowledge to improve logical reasoning, thus resulting in suboptimal performance. In this paper, we propose a complex reasoning schema over KG upon large language models (LLMs), containing a curriculum-based logical-aware instruction tuning framework, named LACT. Specifically, we augment the arbitrary first-order logical queries via binary tree decomposition, to stimulate the reasoning capability of LLMs. To address the difficulty gap among different types of complex queries, we design a simple and flexible logic-aware curriculum learning framework. Experiments across widely used datasets demonstrate that LACT has substantial improvements~(brings an average +5.5% MRR score) over advanced methods, achieving the new state-of-the-art.

Abstract Link prediction is essential for identifying hidden relationships within network data, with significant implications for fields such as social network analysis and bioinformatics. Traditional methods often overlook potential relationships among common neighbors, limiting their effectiveness in utilizing graph information fully. To address this, we introduce a novel approach, Common Neighbor Completion with Information Entropy (IECNC), which enhances model expressiveness by considering logical neighbor relationships. Our method integrates a dynamic node function with a Message Passing Neural Network (MPNN), focusing on first-order neighbors and employing set-based aggregation to improve missing link predictions. By combining the information entropy of probabilistic predictions of common neighbors with MPNN and leveraging information entropy to assess uncertainty in adjacent connections, our approach significantly enhances prediction accuracy. Experimental results demonstrate that our IECNC method achieves optimal performance across multiple datasets, surpassing existing techniques. Furthermore, visualizations confirm that our model effectively captures and accurately learns feature information from various categories, Demonstrating the method’s efficacy and adaptability.

Finding the triangles of large scale graphs is a fundamental graph mining task in many applications, such as motif detection, microscopic evolution, and link prediction. The recent works on triangle counting can be classified into merge-based or binary search-based paradigms. The merge-based triangle counting paradigm locates the triangles using the set intersection operation, which suffers from the random memory access problem. The binary search-based triangle counting paradigm sets the neighbors of the source vertex of an edge as the lookup array and searches the neighbors of the destination vertex. There are lots of expensive lock operations needed in the binary search-based paradigm, which leads to low thread efficiency. In this paper, we aim to improve the triangle counting efficiency on GPU by designing a lock-free policy named Skiff to implement a hash-based triangle counting algorithm. In Skiff, we first design a hash trie data layout to meet the coalesced memory access model and then propose a lock-free policy to reduce the conflicts of the hash trie. In addition, we use a level array to manage the index of the hash trie to make sure the nodes of the hash trie can be quickly located. Furthermore, we implement a CTA thread organization model to reduce the load imbalance of the real-world graphs. We conducted extensive experiments on NVIDIA GPUs to show the performance of Skiff. The results show that Skiff can achieve a good system performance improvement than the state-of-the-art (SOTA) works.

Determining the types of neurons within a nervous system plays a significant role in the analysis of brain connectomics and the investigation of neurological diseases. However, the efficiency of utilizing anatomical, physiological, or molecular characteristics of neurons is relatively low and costly. With the advancements in electron microscopy imaging and analysis techniques for brain tissue, we are able to obtain whole-brain connectome consisting neuronal high-resolution morphology and connectivity information. However, few models are built based on such data for automated neuron classification. In this paper, we propose NeuNet, a framework that combines morphological information of neurons obtained from skeleton and topological information between neurons obtained from neural circuit. Specifically, NeuNet consists of three components, namely Skeleton Encoder, Connectome Encoder, and Readout Layer. Skeleton Encoder integrates the local information of neurons in a bottom-up manner, with a one-dimensional convolution in neural skeleton's point data; Connectome Encoder uses a graph neural network to capture the topological information of neural circuit; finally, Readout Layer fuses the above two information and outputs classification results. We reprocess and release two new datasets for neuron classification task from volume electron microscopy(VEM) images of human brain cortex and Drosophila brain. Experiments on these two datasets demonstrated the effectiveness of our model with accuracies of 0.9169 and 0.9363, respectively. Code and data are available at: https://github.com/WHUminghui/NeuNet.

Recently, multi-hop reasoning over incomplete Knowledge Graphs (KGs) has attracted wide attention due to its desirable interpretability for downstream tasks, such as question answer and knowledge graph completion. Multi-Hop reasoning is a typical sequential decision problem, which can be formulated as a Markov decision process (MDP). Subsequently, some reinforcement learning (RL) based approaches are proposed and proven effective to train an agent for reasoning paths sequentially until reaching the target answer. However, these approaches assume that an entity/relation representation follows a one-point distribution. In fact, different entities and relations may contain different certainties. On the other hand, since REINFORCE used for updating the policy in these approaches is a biased policy gradients method, the agent is prone to be stuck in high reward paths rather than broad reasoning paths, which leads to premature and suboptimal exploitation. In this paper, we consider a Bayesian reinforcement learning paradigm to harness uncertainty into multi-hop reasoning. By incorporating uncertainty into the representation layer, the agent trained by RL has uncertainty in a region of the state space then it should be more efficient in exploring unknown or less known part of the KG. In our approach, we build a Bayesian Q-learning architecture as a state-action value function for estimating the expected long-term reward. As initialized by Gaussian prior or pre-trained prior distribution, the representation layer drives uncertainty that allows regularizing the training. We conducted extensive experiments on multiple KGs. Experimental results show a superior performance than other baselines, especially significant improvements on the automated extracted KG.

Meta-paths are important tools for a wide variety of data mining and network analysis tasks in Heterogeneous Information Networks (HINs), due to their flexibility and interpretability to capture the complex semantic relation among objects. To date, most HIN analysis still relies on hand-crafting meta-paths, which requires rich domain knowledge that is extremely difficult to obtain in complex, large-scale, and schema-rich HINs. In this work, we present a novel framework, Meta-path Discovery with Reinforcement Learning (MPDRL), to identify informative meta-paths from complex and large-scale HINs. To capture different semantic information between objects, we propose a novel multi-hop reasoning strategy in a reinforcement learning framework which aims to infer the next promising relation that links a source entity to a target entity. To improve the efficiency, moreover, we develop a type context representation embedded approach to scale the RL framework to handle million-scale HINs. As multi-hop reasoning generates rich meta-paths with various length, we further perform a meta-path induction step to summarize the important meta-paths using Lowest Common Ancestor principle. Experimental results on two large-scale HINs, Yago and NELL, validate our approach and demonstrate that our algorithm not only achieves superior performance in the link prediction task, but also identifies useful meta-paths that would have been ignored by human experts.

Knowledge Graphs typically suffer from incompleteness. A popular approach to knowledge graph completion is to infer missing knowledge by multihop reasoning over the information found along other paths connecting a pair of entities. However, multi-hop reasoning is still challenging because the reasoning process usually experiences multiple semantic issue that a relation or an entity has multiple meanings. In order to deal with the situation, we propose a novel Hierarchical Reinforcement Learning framework to learn chains of reasoning from a Knowledge Graph automatically. Our framework is inspired by the hierarchical structure through which human handle cognitionally ambiguous cases. The whole reasoning process is decomposed into a hierarchy of two-level Reinforcement Learning policies for encoding historical information and learning structured action space. As a consequence, it is more feasible and natural for dealing with the multiple semantic issue. Experimental results show that our proposed model achieves substantial improvements in ambiguous relation tasks.