Online Discriminative Graph Learning from Multi-Class Smooth Signals

Multi-view feature selection via Nonnegative Structured Graph Learning

Neurocomputing ◽

10.1016/j.neucom.2020.01.044 ◽

2020 ◽

Vol 387 ◽

pp. 110-122 ◽

Cited By ~ 1

Author(s):

Xiangpin Bai ◽

Lei Zhu ◽

Cheng Liang ◽

Jingjing Li ◽

Xiushan Nie ◽

...

Keyword(s):

Feature Selection ◽

Graph Learning

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Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art

Data Science and Engineering ◽

10.1007/s41019-021-00155-3 ◽

2021 ◽

Author(s):

Yun Peng ◽

Byron Choi ◽

Jianliang Xu

Keyword(s):

Machine Learning ◽

Combinatorial Optimization ◽

Graph Embedding ◽

Partial Solution ◽

Complex Data ◽

Learning Methods ◽

Graph Learning ◽

Second Stage ◽

End To End ◽

Embedding Methods

AbstractGraphs have been widely used to represent complex data in many applications, such as e-commerce, social networks, and bioinformatics. Efficient and effective analysis of graph data is important for graph-based applications. However, most graph analysis tasks are combinatorial optimization (CO) problems, which are NP-hard. Recent studies have focused a lot on the potential of using machine learning (ML) to solve graph-based CO problems. Most recent methods follow the two-stage framework. The first stage is graph representation learning, which embeds the graphs into low-dimension vectors. The second stage uses machine learning to solve the CO problems using the embeddings of the graphs learned in the first stage. The works for the first stage can be classified into two categories, graph embedding methods and end-to-end learning methods. For graph embedding methods, the learning of the the embeddings of the graphs has its own objective, which may not rely on the CO problems to be solved. The CO problems are solved by independent downstream tasks. For end-to-end learning methods, the learning of the embeddings of the graphs does not have its own objective and is an intermediate step of the learning procedure of solving the CO problems. The works for the second stage can also be classified into two categories, non-autoregressive methods and autoregressive methods. Non-autoregressive methods predict a solution for a CO problem in one shot. A non-autoregressive method predicts a matrix that denotes the probability of each node/edge being a part of a solution of the CO problem. The solution can be computed from the matrix using search heuristics such as beam search. Autoregressive methods iteratively extend a partial solution step by step. At each step, an autoregressive method predicts a node/edge conditioned to current partial solution, which is used to its extension. In this survey, we provide a thorough overview of recent studies of the graph learning-based CO methods. The survey ends with several remarks on future research directions.

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