Title: Do We Really Need Graph Models for Skeleton-Based Action Recognition? A Topology-Agnostic Approach with Fully-Connected Networks. Abstract: Graph Convolutional Networks (GCNs) have been dominating skeleton-based action recognition in recent years. While GCN-based approaches keep establishing new state-of-the-art results, the proposed architectures are getting increasingly sophisticated with a variety of add-ons. Many recent works attempt to relax the topology restriction imposed by the GCN framework, such as local/sparse connections and permutation invariance. However, the room for further innovation is extremely limited under such a framework. In this work, we present Topology-Agnostic Network (ToANet), a simple architecture based merely on Fully-Connected (FC) layers, as opposed to GCNs for skeleton-based action recognition. It is constructed by chaining FC layers applied across joints (aggregate joint information) and within each joint (transform joint features) in an alternate manner. Moreover, it contains a novel design of parallel paths for multi-relational modeling. ToANet proves to be a powerful architecture for learning the joint co-occurrence of human skeleton data. ToANet achieves better or comparable results to state-of-the-art GCNs on NTU RGB+D, NTU RGB+D 120 and Northwestern-UCLA datasets. These results challenge the convention of choosing GCNs as the de-facto option for skeleton-based action recognition. We hope that our work stimulates further research on non-GCN based methods, eliminating the restriction of topology.