Message Passing Selection Towards Interpretable Gnns For Graph In this paper, we strive to develop an interpretable gnns' inference paradigm, termed msinterpreter, which can serve as a plug and play scheme readily applicable to various gnns' baselines. Wenda li, kaixuan chen, shunyu liu, wenjie huang, haofei zhang, mingli song, yingjie tian, yun su published: 01 jan 2023, last modified: 05 feb 2025tiny papers @ iclr 2023everyone revisions bibtex cc by sa 4.0 loading.
Figure 1 From Message Passing Selection Towards Interpretable Gnns For This paper strives to develop an interpretable gnns' inference paradigm, termed msinterpreter, which can serve as a plug and play scheme readily applicable to various gnn's' baselines, which aims at reaching the self explaination instead of post hoc explanations. Selected publications by categories in reversed chronological order. please refer to my google scholar for the full list. Abstract: in this paper, we strive to develop an interpretable gnns' inference paradigm, termed msinterpreter, which can serve as a plug and play scheme readily applicable to various gnns' baselines. Bibliographic details on message passing selection: towards interpretable gnns for graph classification.
Benchmarking Graph Neural Networks Ntu Graph Deep Learning Lab Abstract: in this paper, we strive to develop an interpretable gnns' inference paradigm, termed msinterpreter, which can serve as a plug and play scheme readily applicable to various gnns' baselines. Bibliographic details on message passing selection: towards interpretable gnns for graph classification. This paper introduces msinterpreter, a plug and play scheme for interpretable gnns that employs a message passing selection scheme to identify critical paths for message aggregation, aiming for self explanation rather than post hoc explanations. Article "message passing selection: towards interpretable gnns for graph classification" detailed information of the j global is an information service managed by the japan science and technology agency (hereinafter referred to as "jst"). Abstract graph neural networks (gnns) have achieved state of the art performance in learning from relational data, yet their decision making processes remain largely opaque. while recent logic based self explainable approaches like logix gin have advanced transparency by grounding logical rules in the model’s architecture, they are fundamentally restricted to unary predicates, limiting their.
Pdf Towards Training Gnns Using Explanation Directed Message Passing This paper introduces msinterpreter, a plug and play scheme for interpretable gnns that employs a message passing selection scheme to identify critical paths for message aggregation, aiming for self explanation rather than post hoc explanations. Article "message passing selection: towards interpretable gnns for graph classification" detailed information of the j global is an information service managed by the japan science and technology agency (hereinafter referred to as "jst"). Abstract graph neural networks (gnns) have achieved state of the art performance in learning from relational data, yet their decision making processes remain largely opaque. while recent logic based self explainable approaches like logix gin have advanced transparency by grounding logical rules in the model’s architecture, they are fundamentally restricted to unary predicates, limiting their.
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