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Structure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs [PDF]
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021 NAACL ...
Jiaao Chen, Diyi Yang
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Generic Structure Extraction with Bi-Level Optimization for Graph Structure Learning
Entropy, 2022 Currently, most Graph Structure Learning (GSL) methods, as a means of learning graph structure, improve the robustness of GNN merely from a local view by considering the local information related to each edge and indiscriminately applying the mechanism ...
Nan Yin, Zhigang Luo
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Graph convolutional networks fusing motif-structure information
Scientific Reports, 2022 With the advent of the wave of big data, the generation of more and more graph data brings great pressure to the traditional deep learning model. The birth of graph neural network fill the gap of deep learning in graph data.
Bin Wang +4 more
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Juggrnaut: Graph Grammar Abstraction for Unbounded Heap Structures
Electronic Notes in Theoretical Computer Science, 2010 AbstractWe present a novel abstraction framework for heap data structures that uses graph grammars, more precisely context-free hyperedge replacement grammars, as an intuitive formalism for efficiently modeling dynamic data structures. It aims at extending finite-state verification techniques to handle pointer-manipulating programs operating on complex
Jonathan Heinen +2 more
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Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions
Advances in Neural Information Processing Systems 36, 2023 The aim of this paper is to make clear and precise the relationship between the Rubin causal model (RCM) and structural causal model (SCM) frameworks for causal inference. Adopting a neutral logical perspective, and drawing on previous work, we show what is required for an RCM to be representable by an SCM.
Duligur Ibeling, Thomas Icard
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CAAI Transactions on Intelligence Technology, 2023 Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently. However, there are still two challenges.
Beibei Han +3 more
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The Maximal Product in Cubic Fuzzy Graph Structures with an Application
International Journal of Computational Intelligence Systems, 2023 The cubic fuzzy graph structure, as a combination of cubic fuzzy graphs and fuzzy graph structures, shows better capabilities in solving complex problems, especially in cases where there are multiple relationships.
Yongsheng Rao +3 more
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A Graph Abstract Machine Describing Event Structure Composition
Electronic Notes in Theoretical Computer Science, 2007 AbstractEvent structures, Game Semantics strategies and Linear Logic proof-nets arise in different domains (concurrency, semantics, proof-theory) but can all be described by means of directed acyclic graphs (dag's). They are all equipped with a specific notion of composition, interaction or normalization.We report on-going work, aiming to investigate ...
C. FAGGIAN, PICCOLO, Mauro
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HyGen: generating random graphs with hyperbolic communities
Applied Network Science, 2019 Random graph generators are necessary tools for many network science applications. For example, the evaluation of graph analysis algorithms requires methods for generating realistic synthetic graphs.
Saskia Metzler, Pauli Miettinen
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Graph wavelet transform for image texture classification
IET Image Processing, 2021 Graph is a data structure that can represent complex relationships among data. Graph signal processing, unlike traditional signal processing, explicitly considers the structure and relationship among the signal samples.
Yu‐Long Qiao +4 more
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