368 research outputs found
Learning-Based Approaches for Graph Problems: A Survey
Over the years, many graph problems specifically those in NP-complete are
studied by a wide range of researchers. Some famous examples include graph
colouring, travelling salesman problem and subgraph isomorphism. Most of these
problems are typically addressed by exact algorithms, approximate algorithms
and heuristics. There are however some drawback for each of these methods.
Recent studies have employed learning-based frameworks such as machine learning
techniques in solving these problems, given that they are useful in discovering
new patterns in structured data that can be represented using graphs. This
research direction has successfully attracted a considerable amount of
attention. In this survey, we provide a systematic review mainly on classic
graph problems in which learning-based approaches have been proposed in
addressing the problems. We discuss the overview of each framework, and provide
analyses based on the design and performance of the framework. Some potential
research questions are also suggested. Ultimately, this survey gives a clearer
insight and can be used as a stepping stone to the research community in
studying problems in this field.Comment: v1: 41 pages; v2: 40 page
Efficient Fuel Consumption Minimization for Green Vehicle Routing Problems using a Hybrid Neural Network-Optimization Algorithm
Efficient routing optimization yields benefits that extend beyond mere financial
gains. In this thesis, we present a methodology that utilizes a graph convolutional neural network to facilitate the development of energy-efficient waste
collection routes. Our approach focuses on a Waste company in Tromsø, Remiks,
and uses real-life datasets, ensuring practicability and ease of implementation.
In particular, we extend the dpdp algorithm introduced by Kool et al. (2021) [1]
to minimize fuel consumption and devise routes that account for the impact of
elevation and real road distance traveled. Our findings shed light on the potential advantages and enhancements these optimized routes can offer Remiks,
including improved effectiveness and cost savings. Additionally, we identify
key areas for future research and development
A Novel Differentiable Loss Function for Unsupervised Graph Neural Networks in Graph Partitioning
In this paper, we explore the graph partitioning problem, a pivotal
combina-torial optimization challenge with extensive applications in various
fields such as science, technology, and business. Recognized as an NP-hard
prob-lem, graph partitioning lacks polynomial-time algorithms for its
resolution. Recently, there has been a burgeoning interest in leveraging
machine learn-ing, particularly approaches like supervised, unsupervised, and
reinforce-ment learning, to tackle such NP-hard problems. However, these
methods face significant hurdles: supervised learning is constrained by the
necessity of labeled solution instances, which are often computationally
impractical to obtain; reinforcement learning grapples with instability in the
learning pro-cess; and unsupervised learning contends with the absence of a
differentia-ble loss function, a consequence of the discrete nature of most
combinatorial optimization problems. Addressing these challenges, our research
introduces a novel pipeline employing an unsupervised graph neural network to
solve the graph partitioning problem. The core innovation of this study is the
for-mulation of a differentiable loss function tailored for this purpose. We
rigor-ously evaluate our methodology against contemporary state-of-the-art
tech-niques, focusing on metrics: cuts and balance, and our findings reveal
that our is competitive with these leading methods.Comment: 2 Tables, 2 Figure
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