Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks

arXiv:2402.12411v1 Announce Type: cross
Abstract: Node importance estimation problem has been studied conventionally with homogeneous network topology analysis. To deal with network heterogeneity, a few recent methods employ graph neural models to automatically learn diverse sources of information. However, the major concern revolves around that their full adaptive learning process may lead to insufficient information exploration, thereby formulating the problem as the isolated node value prediction with underperformance and less interpretability. In this work, we propose a novel learning framework: …

abstract analysis arxiv cs.ai cs.lg cs.si deal diverse exploitation graph importance information knowledge learn major network networks node synergy topology type value