2021年10月17日 · To address this problem, we first design a partition-agnostic polarization measure for signed graphs based on the signed random-walk and show that many real-world graphs are highly polarized. Then, we propose POLE (POLarized Embedding for signed networks), a signed embedding method for polarized …

The proposed method, POLE, captures both topological and signed similarities jointly via signed autocovariance and leverages matrix factorization to generate embedding. It achieves state-of-the-art signed link prediction performance especially for predicting conflicts in …

•We propose POLE, a novel signed embedding approach for polarized graphs that captures both topological and signed similarities via signed autocovariance. •We conduct an extensive experimental evaluation of our method on six real-world signed graphs. Results show that POLE significantly outperforms state-of-the-art methods in

To address this problem, we first design a partition-agnostic polarization measure for signed graphs based on the signed random-walk and show that many real-world graphs are highly polarized. Then, we propose POLE (POLarized Embedding for signed networks), a signed embedding method for polarized graphs that captures both topological and signed ...

2021年10月17日 · Through extensive experiments, we show that POLE significantly outperforms state-of-the-art methods in signed link prediction, particularly for negative links with gains of up to one order of...

2021年10月17日 · To address this problem, we first design a partition-agnostic polarization measure for signed graphs based on the signed random-walk and show that many real-world graphs are highly polarized. Then, we propose POLE (POLarized Embedding for signed networks), a signed embedding method for polarized …

Title: POLE: Polarized Embedding for Signed Networks: Publication Type: Conference Paper: Year of Publication: 2022: Authors: Huang, Z., A. Silva, and A. K. Singh

2021年10月20日 · We propose POLE, a novel signed embedding approach for polarized graphs that captures both topological and signed similarities via signed autocovariance. We conduct an extensive experimental evaluation of our method on six real-world signed graphs.

2022年2月11日 · We propose the latent Signed relational Latent dIstance Model (SLIM) utilizing for the first time the Skellam distribution as a likelihood function for signed networks and extend the modeling...

3-Pole Signed Distance Function (3PSDF) is a learnable implicit representation that is capable of representing surfaces with arbitrary topologies, including open surfaces.