arXiv Machine Learning By Jimmy Dubuisson

Community-Aware Vertex Ordering for Reference-Based Graph Compression: A Cross-Encoder Empirical Study

Read the original on arXiv Machine Learning →

arXiv:2605. 21510v2 Announce Type: replace-cross Abstract: Reference-based graph compression encodes each vertex's neighbor list as differences from a nearby encoded list.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jun 11

GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs

arXiv:2606. 11562v1 Announce Type: new Abstract: Graph analysis underlies many applications whose answers cannot be looked up in a single record or retrieved along a path: laundering rings, drug repurposing, user preference, and scientific theme are all inferred from a node together with its neighbourhood.

By Zhuoyi Peng, Jingzhou Jiang, Hanlin Gu, Lixin Fan, Yi Yang
arXiv AI
Jul 14

Instruction Set and Language for Hypergraphs

arXiv:2607. 10194v1 Announce Type: cross Abstract: We present IsalHG, a method for representing the structure of any finite, connected hypergraph of bounded hyperedge arity as a string over a compact instruction alphabet $\Sigma_{\mathrm{HG}}$.

By Mario Pascual-Gonzalez, Ezequiel Lopez-Rubio
arXiv AI
Sep 3

Codebook Agent: Amortized Topology Design for LLM Multi-Agent Systems

The paper introduces Codebook Agent, a method for adapting the communication topology of large language model (LLM) multi‑agent systems to individual queries. Instead of searching the full adjacency space, it compresses successful topologies into a 16‑entry codebook via a vector‑quantized autoencoder and uses a reward‑weighted MLP to select a code for each query, followed by a single‑pass MLP proxy to rerank candidates. This approach eliminates iterative search and message passing at test time, achieving higher accuracy across six benchmarks, faster topology generation (2.4 ms), and reduced token usage (21.9–33.2 %).

By Jinxi Yu, Yubei Li, Eric Hanchen Jiang, Zhi Zhang, Dong Liu, Wenxiao Zhao, Levina Li, Kai-Wei Chang, Ying Nian Wu