Search Results for author: Goce Trajcevski

Found 7 papers, 4 papers with code

Predicting Human Mobility via Self-supervised Disentanglement Learning

no code implementations17 Nov 2022 Qiang Gao, Jinyu Hong, Xovee Xu, Ping Kuang, Fan Zhou, Goce Trajcevski

However, most of the existing research concentrates on fusing different semantics underlying sequential trajectories for mobility pattern learning which, in turn, yields a narrow perspective on comprehending human intrinsic motions.


Interpreting Twitter User Geolocation

no code implementations ACL 2020 Ting Zhong, Tianliang Wang, Fan Zhou, Goce Trajcevski, Kunpeng Zhang, Yi Yang

Identifying user geolocation in online social networks is an essential task in many location-based applications.

A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances

3 code implementations22 May 2020 Fan Zhou, Xovee Xu, Goce Trajcevski, Kunpeng Zhang

The deluge of digital information in our daily life -- from user-generated content, such as microblogs and scientific papers, to online business, such as viral marketing and advertising -- offers unprecedented opportunities to explore and exploit the trajectories and structures of the evolution of information cascades.

Feature Engineering Marketing

A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific Impact

1 code implementation26 Mar 2020 Fan Zhou, Xovee Xu, Ce Li, Goce Trajcevski, Ting Zhong, Kunpeng Zhang

Quantifying and predicting the long-term impact of scientific writings or individual scholars has important implications for many policy decisions, such as funding proposal evaluation and identifying emerging research fields.

Frosting Weights for Better Continual Training

1 code implementation7 Jan 2020 Xiaofeng Zhu, Feng Liu, Goce Trajcevski, Dingding Wang

Training a neural network model can be a lifelong learning process and is a computationally intensive one.


Meta-GNN: On Few-shot Node Classification in Graph Meta-learning

no code implementations23 May 2019 Fan Zhou, Chengtai Cao, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, Ji Geng

Meta-learning has received a tremendous recent attention as a possible approach for mimicking human intelligence, i. e., acquiring new knowledge and skills with little or even no demonstration.

Few-Shot Learning General Classification +1

Trajectory-User Linking via Variational AutoEncoder

1 code implementation International Joint Conference on Artificial Intelligence 2018 Fan Zhou, Qiang Gao, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, Fengli Zhang

Trajectory-User Linking (TUL) is an essential task in Geo-tagged social media (GTSM) applications, enabling personalized Point of Interest (POI) recommendation and activity identification.

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