Large Language Models (LLMs) demonstrate strong reasoning ability but are constrained by outdated knowledge and hallucinations. Knowledge Graphs (KGs) can help mitigate these limitations by providing structured relational knowledge and capturing global multi-entity, multi-hop dependencies beyond linear text. However, existing KG-enhanced LLM reasoning methods mostly rely on hop-by-hop textual traversal, rather than making multi-hop decisions with a broader perspective as humans do, thus underusing the structural advantages of KGs. This leads to myopic exploration and fragmented reasoning, wasting effort on irrelevant entities while potentially missing critical ones. To address these limitations, we propose Foresight-on-Graph (FoG), a novel framework that introduces visual relation graphs to expand the reasoning horizon at each step. FoG enables Vision-Language Models (VLMs) to simultaneously attend to multi-entity, multi-hop contexts, supporting foresighted planning and more efficient graph exploration. To facilitate effective visual reasoning, we design relation-based fuzzy matching and union-find algorithms to filter out irrelevant relations and coarsen the graph, condensing subgraphs with millions of entities into compact relation graphs with only tens of nodes. Experiments on three real-world datasets demonstrate that FoG consistently outperforms existing methods while significantly improving reasoning efficiency. Code is available at https://github.com/hyworrywart/FoG.

The structural properties of naturally arising social graphs are extensively studied to understand their evolution. Prior approaches for modeling network dynamics typically rely on rule-based models, which lack realism and generalizability, or deep learning-based models, which require large-scale training datasets. As abstract graph representations of entity-wise interactions, social graphs present an opportunity to explore network evolution mechanisms through realistic simulations of human-item interactions. Leveraging the pre-trained social consensus knowledge embedded in large language models (LLMs), we present GraphAgent-Generator (GAG), a novel simulation-based framework for dynamic, text-attributed social graph generation. GAG simulates the temporal node and edge generation processes for zero-shot social graph generation. The resulting graphs adhere to seven key macroscopic network properties, achieving an 11% improvement in microscopic graph structure metrics. Through the node classification benchmarking task, we validate that GAG effectively captures the intricate text-structure correlations in graph generation. Furthermore, GAG supports generating graphs with up to nearly 100,000 nodes or 10 million edges through large-scale LLM-based agent simulation with parallel acceleration, achieving a minimum speed-up of 90.4%. The source code is available at https://github.com/Ji-Cather/GraphAgent.