Low-Rank Representations Towards Classification Problem of Complex Networks
This work addresses the classification of complex networks, such as social or biological systems, for applications like search engines and recommender systems, but it appears incremental as it focuses on evaluating existing low-rank representation methods.
The paper tackled the problem of classifying real-life complex networks by evaluating the performance of low-rank Euclidean embeddings, which represent vertices in a low-dimensional space to predict edges, on a network classification task.
Complex networks representing social interactions, brain activities, molecular structures have been studied widely to be able to understand and predict their characteristics as graphs. Models and algorithms for these networks are used in real-life applications, such as search engines, and recommender systems. In general, such networks are modelled by constructing a low-dimensional Euclidean embedding of the vertices of the network, where proximity of the vertices in the Euclidean space hints the likelihood of an edge (link). In this work, we study the performance of such low-rank representations of real-life networks on a network classification problem.