27 research outputs found
Learning-Based Heuristic for Combinatorial Optimization of the Minimum Dominating Set Problem using Graph Convolutional Networks
A dominating set of a graph is a subset of vertices
such that every vertex
outside the dominating set is adjacent to a vertex within the set. The
minimum dominating set problem seeks to find a dominating set of minimum
cardinality and is a well-established NP-hard combinatorial optimization
problem. We propose a novel learning-based heuristic approach to compute
solutions for the minimum dominating set problem using graph convolutional
networks. We conduct an extensive experimental evaluation of the proposed
method on a combination of randomly generated graphs and real-world graph
datasets. Our results indicate that the proposed learning-based approach can
outperform a classical greedy approximation algorithm. Furthermore, we
demonstrate the generalization capability of the graph convolutional network
across datasets and its ability to scale to graphs of higher order than those
on which it was trained. Finally, we utilize the proposed learning-based
heuristic in an iterative greedy algorithm, achieving state-of-the-art
performance in the computation of dominating sets
Affine-Invariant Outlier Detection and Data Visualization
A wealth of data is generated daily by social media websites that is an essential component of the Big Data Revolution. In many cases, the data is anonymized before being disseminated for research and analysis. This anonymization process distorts the data so that some essential characteristics are lost which may not be captured by methods that are not robust against such transformations. In this paper we propose novel algorithms, for two-dimensional data, for a recently discovered statistical data analysis measure, the Ray Shooting Depth (RSD) that provides an affineinvariant ranking of data points. In addition, we prove some complexity results and illustrate some of the desirable properties of RSD via comparisons with other similar notions. We develop an open-source data visualization tool based on RSD, and show its applications in distribution estimation, outlier detection, and 2D tolerance-region construction
Controllability Backbone in Networks
This paper studies the controllability backbone problem in dynamical networks
defined over graphs. The main idea of the controllability backbone is to
identify a small subset of edges in a given network such that any subnetwork
containing those edges/links has at least the same network controllability as
the original network while assuming the same set of input/leader vertices. We
consider the strong structural controllability (SSC) in our work, which is
useful but computationally challenging. Thus, we utilize two lower bounds on
the network's SSC based on the zero forcing notion and graph distances. We
provide algorithms to compute controllability backbones while preserving these
lower bounds. We thoroughly analyze the proposed algorithms and compute the
number of edges in the controllability backbones. Finally, we compare and
numerically evaluate our methods on random graphs.Comment: Accepted in 62nd IEEE Conference on Decision and Control, Dec. 13-15,
2023, Singapor