In-Person Poster presentation / poster accept

Agent-based Graph Neural Networks

Karolis Martinkus · Pál András Papp · Benedikt Schesch · Roger Wattenhofer

MH1-2-3-4 #52

Keywords: [ Deep Learning and representational learning ] [ graph neural networks ] [ gnn ] [ Sublinear algorithms ] [ Expressive Graph Neural Networks ] [ graph classification ]

[ Abstract ]
[ Poster [ OpenReview
Tue 2 May 2:30 a.m. PDT — 4:30 a.m. PDT


We present a novel graph neural network we call AgentNet, which is designed specifically for graph-level tasks. AgentNet is inspired by sublinear algorithms, featuring a computational complexity that is independent of the graph size. The architecture of AgentNet differs fundamentally from the architectures of traditional graph neural networks. In AgentNet, some trained \textit{neural agents} intelligently walk the graph, and then collectively decide on the output. We provide an extensive theoretical analysis of AgentNet: We show that the agents can learn to systematically explore their neighborhood and that AgentNet can distinguish some structures that are even indistinguishable by 2-WL. Moreover, AgentNet is able to separate any two graphs which are sufficiently different in terms of subgraphs. We confirm these theoretical results with synthetic experiments on hard-to-distinguish graphs and real-world graph classification tasks. In both cases, we compare favorably not only to standard GNNs but also to computationally more expensive GNN extensions.

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