Although Large Language Models (LLMs) have demonstrated potential in processing graphs, they struggle with comprehending graphical structure information through prompts of graph description sequences, especially as the graph size increases.We attribute this challenge to the uneven memory performance of LLMs across different positions in graph description sequences, known as "Positional bias".To address this, we propose GraphInsight, a novel framework aimed at improving LLMs' comprehension of both macro-and micro-level graphical information.GraphInsight is grounded in two key strategies: 1) placing critical graphical information in positions where LLMs exhibit stronger memory performance, and 2) investigating a lightweight external knowledge base for regions with weaker memory performance, inspired by retrieval-augmented generation (RAG).Moreover, GraphInsight explores integrating these two strategies into LLM agent processes for composite graph tasks that require multi-step reasoning.Extensive empirical studies on benchmarks with a wide range of evaluation tasks show that GraphInsight significantly outperforms all other graph description methods (e.g., prompting techniques and reordering strategies) in understanding graph structures of varying sizes.

Knowledge graphs (KGs) are vital for enabling knowledge reasoning across various domains. Recent KG reasoning methods that integrate both global and local information have achieved promising results. However, existing methods often suffer from score over-smoothing, which blurs the distinction between correct and incorrect answers and hinders reasoning effectiveness. To address this, we propose DuetGraph, a **coarse-to-fine** KG reasoning mechanism with **dual-pathway** global-local fusion. DuetGraph tackles over-smoothing by segregating—rather than stacking—the processing of local (via message passing) and global (via attention) information into two distinct pathways, preventing mutual interference and preserving representational discrimination. In addition, DuetGraph introduces a **coarse-to-fine** optimization, which partitions entities into high- and low-score subsets. This strategy narrows the candidate space and sharpens the score gap between the two subsets, which alleviates over-smoothing and enhances inference quality. Extensive experiments on various datasets demonstrate that DuetGraph achieves state-of-the-art (SOTA) performance, with up to an **8.7\%** improvement in reasoning quality and a **1.8$\times$** acceleration in training efficiency.

Cutting-edge platforms of graph neural networks (GNNs), such as DGL and PyG, harness the parallel processing power of GPUs to extract structural information from graph data, achieving state-of-the-art (SOTA) performance in fields such as recommendation systems, knowledge graphs, and bioinformatics. Despite the computational advantages provided by GPUs, these GNN platforms struggle with scalability challenges due to the colossal graphical structures processed and the limited memory capacities of GPUs. In response, this work introduces Capsule, a new out-of-core mechanism for large-scale GNN training. Unlike existing out-of-core GNN systems, which use main or secondary memory as operative memory and use CPU kernels during non-backpropagation computation, Capsule uses GPU memory and GPU kernels. By substantially leveraging the parallelization capabilities of GPUs, Capsule significantly enhances GNN training efficiency. In addition, Capsule can be smoothly integrated to mainstream open-source GNN frameworks, DGL and PyG, in a play-and-plug manner. Through a prototype implementation and comprehensive experiments on real datasets, we demonstrate that Capsule can achieve up to a 12.02× improvement in runtime efficiency, while using only 22.24% of the main memory, compared to SOTA out-of-core GNN systems.

Streaming edge partitioning plays a crucial role in the distributed processing of large-scale web graphs, such as pagerank. The quality of partitioning is of utmost importance and directly affects the runtime cost of distributed graph processing. However, streaming graph clustering, a key component of mainstream streaming edge partitioning, is vertex-centric. This incurs a mismatch with the edge-centric partitioning strategy, necessitating additional post-processing and several graph traversals to transition from vertex-centric clusters to edge-centric partitions. This transition not only adds extra runtime overhead but also risks a decline in partitioning quality. In this paper, we propose a novel algorithm, called ClusPar, to address the problem of streaming edge partitioning. The ClusPar framework consists of two steps, streaming edge clustering and edge cluster partitioning. Different from prior studies, the first step traverses the input graph in a single pass to generate edge-centric clusters, while the second step applies game theory over these edge-centric clusters to produce partitions. Extensive experiments show that ClusPar outperforms the state-of-the-art streaming edge partitioning methods in terms of the partitioning quality, efficiency, and scalability.

In the realm of distributed systems tasked with managing and processing large-scale graph-structured data, optimizing graph partitioning stands as a pivotal challenge. The primary goal is to minimize communication overhead and runtime cost. However, alongside the computational complexity associated with optimal graph partitioning, a critical factor to consider is memory overhead. Real-world graphs often reach colossal sizes, making it impractical and economically unviable to load the entire graph into memory for partitioning. This is also a fundamental premise in distributed graph processing, where accommodating a graph with non-distributed systems is unattainable. Currently, existing streaming partitioning algorithms exhibit a skew-oblivious nature, yielding satisfactory partitioning results exclusively for specific graph types. In this paper, we propose a novel streaming partitioning algorithm, the Skewness-aware Vertex-cut Partitioner (S5P ), designed to leverage the skewness characteristics of real graphs for achieving high-quality partitioning. S5P offers high partitioning quality by segregating the graph's edge set into two subsets, head and tail sets. Following processing by a skewness-aware clustering algorithm, these two subsets subsequently undergo a Stackelberg graph game. Our extensive evaluations conducted on substantial real-world and synthetic graphs demonstrate that, in all instances, the partitioning quality of S5P surpasses that of existing streaming partitioning algorithms, operating within the same load balance constraints. For example, S5P can bring up to a 51% improvement in partitioning quality compared to the top partitioner among the baselines. Lastly, we showcase that the implementation of S5P results in up to an 81% reduction in communication cost and a 130% increase in runtime efficiency for distributed graph processing tasks on PowerGraph.