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
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.
Multimodal attributed graphs connect entities, visual content, language, and observed relations. Learning one foundation across such graphs requires more than compressing each node into a fused Euclidean vector. The representation must preserve entity semantics, construct interaction state from graph neighborhoods, and...
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
Persistent Consistency Self-Distillation (PCSD) is proposed, which derives token-level distillation weights from the local persistence of teacher-favoring signals, and combines adaptive windows with exponentially decayed aggregation to capture persistent relative teacher support.
Chun-Ji Lv, Yang-Guang Wei, Junlin Liu et al.· 2 citations
FedGAMMA is proposed, casting federated multimodal graph foundation learning as a two-stage semantic-structural alignment problem of federated pre-training and prompt-based fine-tuning, and outperforms competitive baselines accross multi-domain datasets on multiple tasks.
Xunkai Li, Guohao Fu, Yuming Ai et al.· 0 citations
CHILL-Harness intervenes at the orchestration layer to enable advantage-guided workflow adaptation, thereby improving reasoning and execution efficiency while preserving task performance and incorporating a success-preserving objective and advantage-margin authorization constraints into CHILL-Harness to promote reliabl...
