Control-based Graph Embeddings with Data Augmentation for Contrastive Learning

Anwar Said; Mudassir Shabbir; Obaid Ullah Ahmad; Waseem Abbas; Xenofon Koutsoukos

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Control-based Graph Embeddings with Data Augmentation for Contrastive Learning

Obaid Ullah Ahmad, Anwar Said, Mudassir Shabbir, Waseem Abbas, Xenofon Koutsoukos

Machine Learning · Submitted 7 Mar 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Compared to the existing methods, we demonstrate that this innovative approach enhances the effectiveness of contrastive learning frameworks, leading to superior results regarding the accuracy of the classification tasks.

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arXiv ID2403.04923 (version 2)

Pith IDLXZQRHR5, Pith's permanent record of this paper and its review

CitationAnwar Said, Mudassir Shabbir, Obaid Ullah Ahmad, Waseem Abbas, Xenofon Koutsoukos. "Control-based Graph Embeddings with Data Augmentation for Contrastive Learning." arXiv:2403.04923 (2024). https://pith.science/paper/LXZQRHR5

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What the abstract itself states, as probabilities: claims first 0.18 · states limitation 0.05 · human participants 0.02 · controlled comparison 0.53 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.91 · mentions code or data availability 0.02.

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arXiv listing abstract v2. Results and the main claim are underlined; show every role.

In this paper, we study the problem of unsupervised graph representation learning by harnessing the control properties of dynamical networks defined on graphs. Our approach introduces a novel framework for contrastive learning, a widely prevalent technique for unsupervised representation learning. A crucial step in contrastive learning is the creation of 'augmented' graphs from the input graphs. Though different from the original graphs, these augmented graphs retain the original graph's structural characteristics. Here, we propose a unique method for generating these augmented graphs by leveraging the control properties of networks. The core concept revolves around perturbing the original graph to create a new one while preserving the controllability properties specific to networks and graphs. Compared to the existing methods, we demonstrate that this innovative approach enhances the effectiveness of contrastive learning frameworks, leading to superior results regarding the accuracy of the classification tasks. The key innovation lies in our ability to decode the network structure using these control properties, opening new avenues for unsupervised graph representation learning.

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What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	In this paper, we study the problem of unsupervised graph representation learning by harnessing the control properties of dynamical networks defined on graphs.	contribution 0.52	0.57	0.04	asserted 0.90
s1	Our approach introduces a novel framework for contrastive learning, a widely prevalent technique for unsupervised representation learning.	contribution 0.88	0.67	0.04	asserted 1.00
s2	A crucial step in contrastive learning is the creation of 'augmented' graphs from the input graphs.	background 0.95	0.06	0.02	asserted 1.00
s3	Though different from the original graphs, these augmented graphs retain the original graph's structural characteristics.	background 0.85	0.18	0.02	asserted 1.00
s4	Here, we propose a unique method for generating these augmented graphs by leveraging the control properties of networks.	method 0.51	0.85	0.04	asserted 1.00
s5	The core concept revolves around perturbing the original graph to create a new one while preserving the controllability properties specific to networks and graphs.	method 0.94	0.57	0.06	asserted 0.99
s6	Compared to the existing methods, we demonstrate that this innovative approach enhances the effectiveness of contrastive learning frameworks, leading to superior results regarding the accuracy of the classification tasks.	result 1.00	0.94	0.84	asserted 1.00
s7	The key innovation lies in our ability to decode the network structure using these control properties, opening new avenues for unsupervised graph representation learning.	contribution 0.55	0.72	0.18	asserted 0.95

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.48.

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arXiv listing abstract v2

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

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Original abstract

In this paper, we study the problem of unsupervised graph representation learning by harnessing the control properties of dynamical networks defined on graphs. Our approach introduces a novel framework for contrastive learning, a widely prevalent technique for unsupervised representation learning. A crucial step in contrastive learning is the creation of 'augmented' graphs from the input graphs. Though different from the original graphs, these augmented graphs retain the original graph's structural characteristics. Here, we propose a unique method for generating these augmented graphs by leveraging the control properties of networks. The core concept revolves around perturbing the original graph to create a new one while preserving the controllability properties specific to networks and graphs. Compared to the existing methods, we demonstrate that this innovative approach enhances the effectiveness of contrastive learning frameworks, leading to superior results regarding the accuracy of the classification tasks. The key innovation lies in our ability to decode the network structure using these control properties, opening new avenues for unsupervised graph representation learning.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it

Cites it as background 1 paper

The controllability Gramian is a powerful mathematical concept that plays a crucial role in understanding the control behavior of a network [ref] .

Improving Graph Machine Learning Performance Through Feature Augmentation Based on Network Control Theory · Network Controllability Metrics · 0.89

What it cites · 6 sentences without a confident label

Uses the method or tool of 1 paper

These features are utilized to bring an augmented version $G^'$ of a graph $G$ closer together in a latent space $z(.)$ using the normalized temperature-scaled cross-entropy loss (NT-Xent) [ref] .

Representation Learning with Contrastive Predictive Coding · Proposed Approach · 0.98

Compares with 2 papers

In this comparative study, we've chosen two state-of-the-art contenders, InfoGraph [ref] and GraphCL [ref] , which both employ graph neural networks as their foundational architecture.

InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization · Comparison with the State-of-the-art Methods · 0.97
In this comparative analysis, we have chosen three prominent unsupervised benchmarks methods: node2vec [ref] , sub2vec [ref] , and graph2vec [ref] .

graph2vec: Learning Distributed Representations of Graphs · Comparison with the State-of-the-art Methods · 0.97

Cites as background 3 papers

GCRL encourages the model to bring similar nodes or subgraphs closer together in the embedding space while pushing dissimilar nodes or subgraphs farther apart [ref] , enhancing performance in various downstream tasks [ref] .

InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization · Proposed Approach · 0.81
In contrast, contrastive learning emerges as a self-supervised learning (SSL) technique that has gained prominence in unsupervised network representation learning [ref] .

Representation Learning with Contrastive Predictive Coding · Introduction · 0.93
Networks are ubiquitous data structures used to represent relationships, connectivity, and interactions in a wide range of domains, including social networks, biology, transportation, brain connections, and recommendation systems [ref] .

NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics · Introduction · 1.00

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