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A survey on graph kernels
Graph kernels have become an established and widely-used technique for solving
classification tasks on graphs. This survey gives a comprehensive overview of techniques …
classification tasks on graphs. This survey gives a comprehensive overview of techniques …
Computing graph neural networks: A survey from algorithms to accelerators
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …
years owing to their capability to model and learn from graph-structured data. Such an ability …
Graph of thoughts: Solving elaborate problems with large language models
Abstract We introduce Graph of Thoughts (GoT): a framework that advances prompting
capabilities in large language models (LLMs) beyond those offered by paradigms such as …
capabilities in large language models (LLMs) beyond those offered by paradigms such as …
Improving graph neural network expressivity via subgraph isomorphism counting
While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of
applications, recent studies exposed important shortcomings in their ability to capture the …
applications, recent studies exposed important shortcomings in their ability to capture the …
Characteristic functions on graphs: Birds of a feather, from statistical descriptors to parametric models
In this paper, we propose a flexible notion of characteristic functions defined on graph
vertices to describe the distribution of vertex features at multiple scales. We introduce …
vertices to describe the distribution of vertex features at multiple scales. We introduce …
Graph matching networks for learning the similarity of graph structured objects
This paper addresses the challenging problem of retrieval and matching of graph structured
objects, and makes two key contributions. First, we demonstrate how Graph Neural …
objects, and makes two key contributions. First, we demonstrate how Graph Neural …
Heterogeneous graph neural networks analysis: a survey of techniques, evaluations and applications
Abstract Graph Neural Networks (GNNs) have achieved excellent performance of graph
representation learning and attracted plenty of attentions in recent years. Most of GNNs aim …
representation learning and attracted plenty of attentions in recent years. Most of GNNs aim …
Simgnn: A neural network approach to fast graph similarity computation
Graph similarity search is among the most important graph-based applications, eg finding
the chemical compounds that are most similar to a query compound. Graph …
the chemical compounds that are most similar to a query compound. Graph …
Deep graph kernels
In this paper, we present Deep Graph Kernels, a unified framework to learn latent
representations of sub-structures for graphs, inspired by latest advancements in language …
representations of sub-structures for graphs, inspired by latest advancements in language …
word2vec, node2vec, graph2vec, x2vec: Towards a theory of vector embeddings of structured data
Vector representations of graphs and relational structures, whether hand-crafted feature
vectors or learned representations, enable us to apply standard data analysis and machine …
vectors or learned representations, enable us to apply standard data analysis and machine …