We demonstrate ExGIS , a parallel inference query engine to support explainable Graph Neural Network (GNNs) inference analysis in large graphs. (1) For a class of GNNs ℳ L with at most L layers, and a graph G , ExGIS performs an offline, once-for-all compression of G to a small graph G c , such that for any inference query Q that requests the output of any GNN M ∈ ℳ L on any node v in G, G c can be directly queried to yield correct output without decompression. (2) Given a workload W of inference queries that requests the output of GNNs from ℳ over G , ExGIS perform fast online GNN inference and interpretation in parallel. It dynamically partitions W to balance workloads, and (a) executes inference that only consults compressed graph G c without decompression, and (b) directly yields concise, explanatory subgraphs from G c that can clarify the query output with high fidelity, all in parallel. Moreover, ExGIS integrates visual, interactive interfaces for query performance analysis, and a Large Language Models (LLMs)- enabled interpreter to support user-friendly, natural language explanation of query outputs. We demonstrate the compression rate and scalability of ExGIS, and its application in interpretable anomaly detection over bitcoin transaction networks and academic networks.

We propose a novel framework, On-Demand MOtion Generation (ODMO), for generating realistic and diverse long-term 3D human motion sequences conditioned only on action types with an additional capability of customization. ODMO shows improvements over SOTA approaches on all traditional motion evaluation metrics when evaluated on three public datasets (HumanAct12, UESTC, and MoCap). Furthermore, we provide both qualitative evaluations and quantitative metrics demonstrating several first-known customization capabilities afforded by our framework, including mode discovery, interpolation, and trajectory customization. These capabilities significantly widen the spectrum of potential applications of such motion generation models. The novel on-demand generative capabilities are enabled by innovations in both the encoder and decoder architectures: (i) Encoder: Utilizing contrastive learning in low-dimensional latent space to create a hierarchical embedding of motion sequences, where not only the codes of different action types form different groups, but within an action type, codes of similar inherent patterns (motion styles) cluster together, making them readily discoverable; (ii) Decoder: Using a hierarchical decoding strategy where the motion trajectory is reconstructed first and then used to reconstruct the whole motion sequence. Such an architecture enables effective trajectory control. Our code is released on the Github page: https://github.com/roychowdhuryresearch/ODMO https://github.com/roychowdhuryresearch/ODMO