GRASP
Research - Novi Sad, Vojvodina, Serbia
GRASP Employees
GRASP Details
In today's world, data in graph and tabular form are being generated at astonishing rates, with algorithms for machine learning (ML) and data mining (DM) applied to such data establishing themselves as drivers of modern society. The field of graph embedding is concerned with bridging the "two worlds" of graph data (represented by nodes and edges) and tabular data (represented by rows and columns) by providing means for mapping graphs to tabular data sets, thus unlocking the use of a wide range of tabular ML and DM techniques on graphs. Graph embedding enjoys increased popularity in recent years, with a plethora of new methods being proposed. However, none of them address the dimensionality (number of columns) of the new data space with any sort of depth, which is surprising since it is widely known that dimensionalities greater than 10–15 can lead to adverse effects on tabular ML and DM methods, collectively termed the "curse of dimensionality." This project will investigate the impact of the curse of dimensionality on graph-embedding methods by using two well-studied artefacts of high-dimensional tabular data: (1) hubness (highly connected nodes in nearest-neighbor graphs obtained from tabular data) and (2) local intrinsic dimensionality (LID – number of dimensions needed to express all information near a data point). After evaluating existing graph-embedding methods w.r.t. hubness and LID, we will design new methods that take those factors into account. We expect the project to produce graph-embedding methods substantially more accurate than the state-of-the-art in two aspects: (1) graph reconstruction in the new space, and (2) success of applications of the produced tabular data to the tasks of classification, clustering, similarity search and link prediction. The proposed methods will introduce graph embedding to many domains where such solutions were simply not good enough so far, opening new avenues for researchers and providing new tools for practitioners.
GRASP Technologies
Widgets
Facebook Widget
Load Balancers
Nginx
CMS
WordPress.org
Social Login
Facebook Login (Connect)
Other
Mobile Friendly
Online Video Platforms
YouTube