The rapid accumulation of multi-modal data (e.g., text, images, and geo-locations) presents significant opportunities for data mining applications in healthcare and e-commerce. However, effectively retrieving such data remains challenging due to the difficulty in capturing diverse and dynamic user retrieval intents. Existing solutions, such as vector databases, typically rely on static embeddings or fixed weights, failing to adapt to users' varying preferences across different modalities. To address this, we present OneDB, a distributed framework for multi-metric similarity search that integrates data management with learning-based techniques. Unlike traditional systems, OneDB features three key algorithmic innovations: (i) an adaptive metric weight learning model based on lightweight contrastive learning, which infers implicit user preferences from limited query examples; (ii) a dual-layer indexing strategy that combines global partitioning with modality-aware local indexing to handle heterogeneous data distributions efficiently; and (iii) an end-to-end parameter tuning module leveraging deep reinforcement learning to optimize system performance in dynamic environments. Extensive experiments on real-world datasets demonstrate that OneDB captures user intent effectively, achieving 12.63%--30.75% higher accuracy and 2.5--5.75× faster retrieval speeds compared to state-of-the-art vector search systems.
Tang Qian, Yifan Zhu, Lu Chen et al.· Proceedings of the 32nd ACM...· 0 citations
In this paper, for the first time, we study the community search problem over multimodal graphs. This task aims to identify a query vertex-containing subgraph that is both structurally cohesive and semantically coherent with multimodal query inputs (e.g., text and images). Existing community search methods fail to capture fine-grained multimodal semantics and do not support effective multimodal fusion. To address these limitations, we propose an adaptive community search framework ECHO, which includes two key components. (i) A Fine-grained Modality Extractor decomposes multimodal content into structured local semantic units to preserve details often lost in coarse representations, operating in an encoder-agnostic manner. (ii) A Dual-Track Mixture of Experts network decouples semantic and structural modeling into parallel tracks, utilizing a hierarchical MoE architecture for adaptive, query-aware feature fusion. Extensive experiments on real-world multimodal graphs demonstrate that ECHO consistently outperforms state-of-the-art methods in terms of community quality while achieving superior search efficiency.
Chengyang Luo, Zi-Xing Ding, Qing Liu et al.· Proceedings of the 32nd ACM...· 0 citations
In this paper, for the first time, we study the community search problem over multimodal graphs. This task aims to identify a query vertex-containing subgraph that is both structurally cohesive and semantically coherent with multimodal query inputs (e.g., text and images). Existing community search methods fail to capture fine-grained multimodal semantics and do not support effective multimodal fusion. To address these limitations, we propose an adaptive community search framework ECHO, which includes two key components. (i) A Fine-grained Modality Extractor decomposes multimodal content into structured local semantic units to preserve details often lost in coarse representations, operating in an encoder-agnostic manner. (ii) A Dual-Track Mixture of Experts network decouples semantic and structural modeling into parallel tracks, utilizing a hierarchical MoE architecture for adaptive, query-aware feature fusion. Extensive experiments on real-world multimodal graphs demonstrate that ECHO consistently outperforms state-of-the-art methods in terms of community quality while achieving superior search efficiency.
Chengyang Luo, Zi-Xing Ding, Qing Liu et al.· Proceedings of the 32nd ACM...· 0 citations
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