4 research outputs found
Multi-Level Graph Matching Networks for Deep Graph Similarity Learning
While the celebrated graph neural networks yield effective representations
for individual nodes of a graph, there has been relatively less success in
extending to the task of graph similarity learning. Recent work on graph
similarity learning has considered either global-level graph-graph interactions
or low-level node-node interactions, ignoring the rich cross-level interactions
(e.g., between nodes of a graph and the other whole graph). In this paper, we
propose a Multi-Level Graph Matching Network (MGMN) framework for computing the
graph similarity between any pair of graph-structured objects in an end-to-end
fashion. The proposed model MGMN consists of a node-graph matching network for
effectively learning cross-level interactions between nodes of a graph and the
other whole graph, and a siamese graph neural network to learn global-level
interactions between two input graphs. Furthermore, to bridge the gap of the
lack of standard graph similarity learning benchmarks, we have created and
collected a set of datasets for both the graph-graph classification and
graph-graph regression tasks with different sizes in order to evaluate the
effectiveness and robustness of our models. Comprehensive experiments
demonstrate that the proposed model MGMN consistently outperforms
state-of-the-art baseline models one both the graph-graph classification and
graph-graph regression tasks. Compared with previous work, MGMN also exhibits
stronger robustness as the sizes of the two input graphs increase.Comment: 14 pages, rename names & fix typos, under revie
Fast Detection of Maximum Common Subgraph via Deep Q-Learning
Detecting the Maximum Common Subgraph (MCS) between two input graphs is
fundamental for applications in biomedical analysis, malware detection, cloud
computing, etc. This is especially important in the task of drug design, where
the successful extraction of common substructures in compounds can reduce the
number of experiments needed to be conducted by humans. However, MCS
computation is NP-hard, and state-of-the-art exact MCS solvers do not have
worst-case time complexity guarantee and cannot handle large graphs in
practice. Designing learning based models to find the MCS between two graphs in
an approximate yet accurate way while utilizing as few labeled MCS instances as
possible remains to be a challenging task. Here we propose RLMCS, a Graph
Neural Network based model for MCS detection through reinforcement learning.
Our model uses an exploration tree to extract subgraphs in two graphs one node
pair at a time, and is trained to optimize subgraph extraction rewards via Deep
Q-Networks. A novel graph embedding method is proposed to generate state
representations for nodes and extracted subgraphs jointly at each step.
Experiments on real graph datasets demonstrate that our model performs
favorably to exact MCS solvers and supervised neural graph matching network
models in terms of accuracy and efficiency
Deep Graph Matching and Searching for Semantic Code Retrieval
Code retrieval is to find the code snippet from a large corpus of source code
repositories that highly matches the query of natural language description.
Recent work mainly uses natural language processing techniques to process both
query texts (i.e., human natural language) and code snippets (i.e., machine
programming language), however neglecting the deep structured features of query
texts and source codes, both of which contain rich semantic information. In
this paper, we propose an end-to-end deep graph matching and searching (DGMS)
model based on graph neural networks for the task of semantic code retrieval.
To this end, we first represent both natural language query texts and
programming language code snippets with the unified graph-structured data, and
then use the proposed graph matching and searching model to retrieve the best
matching code snippet. In particular, DGMS not only captures more structural
information for individual query texts or code snippets but also learns the
fine-grained similarity between them by cross-attention based semantic matching
operations. We evaluate the proposed DGMS model on two public code retrieval
datasets with two representative programming languages (i.e., Java and Python).
Experiment results demonstrate that DGMS significantly outperforms
state-of-the-art baseline models by a large margin on both datasets. Moreover,
our extensive ablation studies systematically investigate and illustrate the
impact of each part of DGMS.Comment: Accepted by ACM Transactions on Knowledge Discovery from Data (ACM
TKDD
Deep Graph Similarity Learning: A Survey
In many domains where data are represented as graphs, learning a similarity
metric among graphs is considered a key problem, which can further facilitate
various learning tasks, such as classification, clustering, and similarity
search. Recently, there has been an increasing interest in deep graph
similarity learning, where the key idea is to learn a deep learning model that
maps input graphs to a target space such that the distance in the target space
approximates the structural distance in the input space. Here, we provide a
comprehensive review of the existing literature of deep graph similarity
learning. We propose a systematic taxonomy for the methods and applications.
Finally, we discuss the challenges and future directions for this problem