no code implementations • 20 Jan 2025 • Wan He, Daniel I. Bolnick, Samuel V. Scarpino, Tina Eliassi-Rad
Analysis of single-cell RNA sequencing data is often conducted through network projections such as coexpression networks, primarily due to the abundant availability of network analysis tools for downstream tasks.
1 code implementation • 9 Dec 2024 • Wan He, Tina Eliassi-Rad, Samuel V. Scarpino
We demonstrate the global health potential of the GMNA by leveraging the SARS-CoV-2 genome misclassification networks to investigate the role human mobility played in structuring geographic clustering of SARS-CoV-2.
1 code implementation • 7 Dec 2024 • Zohair Shafi, Germans Savcisens, Tina Eliassi-Rad
In this work, we introduce Radius Enhanced Graph Embeddings (REGE), an approach that measures and incorporates uncertainty in data to produce graph embeddings with radius values that represent the uncertainty of the model's output.
no code implementations • 11 Jun 2024 • Zohair Shafi, Ayan Chatterjee, Tina Eliassi-Rad
(Q2) How can we modify existing node embedding algorithms to produce embeddings that can be easily explained by human-understandable graph features?
no code implementations • 30 Apr 2024 • David Liu, Arjun Seshadri, Tina Eliassi-Rad, Johan Ugander
In this work, we show that node-wise repulsion is, in aggregate, an approximate re-centering of the node embedding dimensions.
no code implementations • 15 Oct 2023 • David Liu, Jackie Baek, Tina Eliassi-Rad
The first negatively impacts less popular items, due to the fact that less popular items rely on trailing latent components to recover their values.
no code implementations • 12 Oct 2023 • Zohair Shafi, Benjamin A. Miller, Ayan Chatterjee, Tina Eliassi-Rad, Rajmonda S. Caceres
We consider an APX-hard problem, where an adversary aims to attack shortest paths in a graph by removing the minimum number of edges.
1 code implementation • 12 Oct 2023 • Zohair Shafi, Benjamin A. Miller, Tina Eliassi-Rad, Rajmonda S. Caceres
Machine learning (ML) approaches are increasingly being used to accelerate combinatorial optimization (CO) problems.
no code implementations • 17 Jul 2023 • Ayan Chatterjee, Robin Walters, Giulia Menichetti, Tina Eliassi-Rad
Our proposed method, UPNA (Unsupervised Pre-training of Node Attributes), solves the inductive link prediction problem by learning a function that takes a pair of node attributes and predicts the probability of an edge, as opposed to Graph Neural Networks (GNN), which can be prone to topological shortcuts in graphs with power-law degree distribution.
no code implementations • 23 Jun 2023 • Dino Pedreschi, Luca Pappalardo, Emanuele Ferragina, Ricardo Baeza-Yates, Albert-Laszlo Barabasi, Frank Dignum, Virginia Dignum, Tina Eliassi-Rad, Fosca Giannotti, Janos Kertesz, Alistair Knott, Yannis Ioannidis, Paul Lukowicz, Andrea Passarella, Alex Sandy Pentland, John Shawe-Taylor, Alessandro Vespignani
Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature.
2 code implementations • 5 Jun 2023 • Germans Savcisens, Tina Eliassi-Rad, Lars Kai Hansen, Laust Mortensen, Lau Lilleholt, Anna Rogers, Ingo Zettler, Sune Lehmann
We can also represent human lives in a way that shares this structural similarity to language.
no code implementations • 10 May 2023 • Dániel L Barabási, Ginestra Bianconi, Ed Bullmore, Mark Burgess, SueYeon Chung, Tina Eliassi-Rad, Dileep George, István A. Kovács, Hernán Makse, Christos Papadimitriou, Thomas E. Nichols, Olaf Sporns, Kim Stachenfeld, Zoltán Toroczkai, Emma K. Towlson, Anthony M Zador, Hongkui Zeng, Albert-László Barabási, Amy Bernard, György Buzsáki
We explore the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease, and discuss the potential for collaboration between network science and neuroscience communities.
no code implementations • 23 May 2022 • Talha Ongun, Simona Boboila, Alina Oprea, Tina Eliassi-Rad, Jason Hiser, Jack Davidson
In this study, we propose CELEST (CollaborativE LEarning for Scalable Threat detection, a federated machine learning framework for global threat detection over HTTP, which is one of the most commonly used protocols for malware dissemination and communication.
2 code implementations • 25 Dec 2021 • Ayan Chatterjee, Robin Walters, Zohair Shafi, Omair Shafi Ahmed, Michael Sebek, Deisy Gysi, Rose Yu, Tina Eliassi-Rad, Albert-László Barabási, Giulia Menichetti
Identifying novel drug-target interactions (DTI) is a critical and rate limiting step in drug discovery.
no code implementations • 16 Mar 2021 • David Liu, Zohair Shafi, William Fleisher, Tina Eliassi-Rad, Scott Alfeld
We present RAWLSNET, a system for altering Bayesian Network (BN) models to satisfy the Rawlsian principle of fair equality of opportunity (FEO).
1 code implementation • 12 Aug 2020 • Sixie Yu, Leonardo Torres, Scott Alfeld, Tina Eliassi-Rad, Yevgeniy Vorobeychik
However, in many applications, such as targeted vulnerability assessment or clinical therapies, one aspires to affect a targeted subset of a network, while limiting the impact on the rest.
Social and Information Networks Physics and Society
no code implementations • 4 Jun 2020 • Leo Torres, Ann S. Blevins, Danielle S. Bassett, Tina Eliassi-Rad
At each step we consider different types of \emph{dependencies}; these are properties of the system that describe how the existence of one relation among the parts of a system may influence the existence of another relation.
Social and Information Networks Discrete Mathematics Quantitative Methods 68R10
no code implementations • 12 Mar 2020 • Benjamin A. Miller, Mustafa Çamurcu, Alexander J. Gomez, Kevin Chan, Tina Eliassi-Rad
Vertex classification is vulnerable to perturbations of both graph topology and vertex attributes, as shown in recent research.
1 code implementation • 9 Jan 2020 • Timothy LaRock, Timothy Sakharov, Sahely Bhadra, Tina Eliassi-Rad
We call this querying process Network Online Learning and present a family of algorithms called NOL*.
no code implementations • 16 Sep 2019 • Peter Morales, Rajmonda Sulo Caceres, Tina Eliassi-Rad
As a result, network discovery algorithms optimized for specific downstream learning tasks given resource collection constraints are of great interest.
1 code implementation • 13 Aug 2019 • Xindi Wang, Onur Varol, Tina Eliassi-Rad
In its placing phase, L2P obtains a prediction by placing the new instance among the known instances.
no code implementations • 10 Jul 2019 • Talha Ongun, Timothy Sakharaov, Simona Boboila, Alina Oprea, Tina Eliassi-Rad
Machine learning (ML) started to become widely deployed in cyber security settings for shortening the detection cycle of cyber attacks.
1 code implementation • 25 May 2019 • Timothy LaRock, Vahan Nanumyan, Ingo Scholtes, Giona Casiraghi, Tina Eliassi-Rad, Frank Schweitzer
Anomaly detection has been extensively studied in categorical sequences, however we often have access to time series data that contain paths through networks.
Social and Information Networks Physics and Society
3 code implementations • 23 May 2019 • Leo Torres, Kevin S. Chan, Tina Eliassi-Rad
Graph embedding seeks to build a low-dimensional representation of a graph G. This low-dimensional representation is then used for various downstream tasks.
no code implementations • 9 Sep 2016 • Sean Gilpin, Chia-Tung Kuo, Tina Eliassi-Rad, Ian Davidson
Role discovery in graphs is an emerging area that allows analysis of complex graphs in an intuitive way.
no code implementations • 12 Sep 2012 • Michele Berlingerio, Danai Koutra, Tina Eliassi-Rad, Christos Faloutsos
Having such features will enable a wealth of graph mining tasks, including clustering, outlier detection, visualization, etc.
Social and Information Networks Physics and Society Applications