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Authors whose works are in public domain in at least one jurisdiction

List of works by Tina Eliassi-Rad

1-28 of 28 results

Collective Classification in Network Data

scientific article published on 6 September 2008

It's who you know

Visual analysis of large heterogeneous social networks by semantic and structural abstraction

scientific article

RolX: structural role extraction & mining in large graphs

article published in 2012

Two Heads Better Than One: Pattern Discovery in Time-Evolving Multi-aspect Data

Network similarity via multiple social theories

Fast best-effort pattern matching in large attributed graphs

Using sequences of life-events to predict human lives

scientific article published on 18 December 2023

GRAPHITE: A Visual Query System for Large Graphs

Gelling, and melting, large graphs by edge manipulation

Gateway finder in large graphs: problem definitions and fast solutions

Fast mining of complex time-stamped events

scientific article

Characterizing collective physical distancing in the U.S. during the first nine months of the COVID-19 pandemic

scholarly article

Measuring algorithmically infused societies

scientific article

AFRAID

Current and Future Challenges in Mining Large Networks

scientific article published in 2016

Fighting a Pandemic

scientific article published on 20 August 2020

Success in books: predicting book sales before publication

scholarly article

GLEE: Geometric Laplacian Eigenmap Embedding

scholarly article

Guilt-by-Constellation: Fraud Detection by Suspicious Clique Memberships

HCDF: A Hybrid Community Discovery Framework

Non-backtracking cycles: length spectrum theory and graph mining applications

scholarly article

Using ghost edges for classification in sparsely labeled networks

Metric forensics

APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions

Two heads better than one: pattern discovery in time-evolving multi-aspect data

BASSET: Scalable Gateway Finder in Large Graphs

On the Vulnerability of Large Graphs