Graph Convolutional Networks (GCNs) are widely used for large graph-structured data, including social, citation, and e-commerce networks, but their deployment is constrained by irregular memory access and severe GPU workload imbalance. These challenges arise in two dimensions: width imbalance from power-law degree dist...
Lingling Zhang, Hang Zeng, Pengpeng Qiao et al.· 0 citations
Negative sampling is a key component of implicit collaborative filtering (CF), as it enables recommenders to effectively learn user preferences. Existing negative sampling methods mostly follow a two-stage paradigm: they first construct a candidate negative pool for each user and then select negative samples from the p...
Jia-Yi Wu, Zheng-Yu Wu, Xun-Kai Li et al.· 0 citations
Multimodal graph foundation models (MGFMs) seek to learn generalizable representations from large-scale graphs with heterogeneous node modalities. However, real-world Multimodal-Attributed Graphs (MAGs) often contain incomplete node attributes, limiting the scale and diversity of available pretraining corpora. Besides,...
Xun-Kai Li, Chen-Xi Wan, Yin-Lin Zhu et al.· 0 citations
Resistance distance computation is a fundamental problem in graph data management. To avoid the high latency of online evaluation, recent work builds offline indexes. Existing approaches, however, fall into two families with complementary weaknesses: loop-erased random-walk methods (e.g., LEIndex) are efficient on fa...
Mei-Hao Liao, Bao Xing, Rong-Hua Li et al.· Proceedings of the ACM on Ma...· 0 citations
Density decomposition characterizes the multi-level dense structure of large networks and supports a wide range of graph mining applications. Given a graph
G = (V, E)
, it assigns each vertex an integral dense number (IDN) and produces a nested sequence of layers D
0
⊇ D
1
⊇ ... ⊇ D
p
that capture increasingl...
Ya-Long Zhang, Rong-Hua Li, Qi Zhang et al.· Proceedings of the ACM on Ma...· 0 citations
Multimodal graph learning faces a fundamental challenge: new classes may emerge after deployment, while models are trained with a fixed label space. Existing approaches typically detect unknown nodes and use LLMs to generate candidate class descriptions, but they do not determine whether existing classes are insufficie...
Ze-Kai Chen, Jia-Yang Xing, Xun Wu et al.· 0 citations
Diffusion planners exhibit strong capabilities in generating multimodal trajectories. However, existing methods primarily rely on expert demonstrations to fit trajectory distributions, learning statistical correlations among scenes, behaviors, and trajectories without explicitly modeling driving rules. In long-tail sce...
Jia-Xi Ye, Chun-Ji Lv, Guo-Ren Wang et al.· 0 citations
OmniVCBench is introduced, a figure-centric, source-traceable benchmark for the interpretation component of an AIVC, and AIVC-Judge is introduced, a task-conditioned MLLM-as-a-judge framework with category-specific, reference-aware rubrics for evaluating open-ended responses.
Man-Yu Li, Xun-Kai Li, Yong-Fu Xiong et al.· 0 citations
Graph-Optimized Multimodal Alignment (GOMA), which lets a jointly trained model support single-modality and dual-attribute retrieval through a task-specific readout, achieves state-of-the-art performance on all 14 primary measures against 14 external methods.
This work introduces ICE (Interaction-aware Clifford Encoder), a multimodal graph foundation model built on a node-indexed Clifford latent field that ranks first in all 30 reported supervised and few-shot comparisons.
Xun-Kai Li, Xu Wang, Yin-Lin Zhu et al.· 0 citations
With the development of sharded blockchains, account migration mechanisms migrate accounts selected by account partition algorithms from the source shard to the target shard, aiming to reduce cross-shard transactions and balance the load. Moreover, the cost of account migration plays a critical role in determining the...
Shuai Zhao, Zhiwei Zhang, Jun-Kai Wang et al.· IEEE Transactions on Knowled...· 0 citations
SpectraMancer, which learns kernels directly in the Fourier spectral domain induced by multilevel circulant matrices, thereby enabling generalizable kernel learning for complex data and improves spectrum-aware kernel selection and predictive performance across diverse benchmarks.
Li-Zhong Ding, Jiarun Fu, Qiuning Wei et al.· IEEE Transactions on Neural...· 0 citations
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