About

Tina Eliassi-Rad is the inaugural Joseph E. Aoun Professor at Northeastern University, based in Boston. She is a core faculty member at Northeastern's Network Science Institute and the Institute for Experiential AI, as well as an external faculty member at the Santa Fe Institute and the Vermont Complex Systems Center. Her research is at the intersection of data mining, machine learning, and network science. She has authored more than 100 peer-reviewed publications, including best paper awards, and has delivered over 200 invited talks and 14 tutorials. Eliassi-Rad's work has been applied to various domains such as personalized searches on the World Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, cyber situational awareness, drug discovery, democracy and online discourse, and ethics in machine learning. Her algorithms have been incorporated into systems used by governments and industry, including IBM System G Graph Analytics, as well as open-source software like the Stanford Network Analysis Project. She has served as program co-chair for major conferences including the ACM International Conference on Knowledge Discovery and Data Mining, the International Conference on Network Science, and the International Conference on Computational Social Science. Her accolades include an Outstanding Mentor Award from the US Department of Energy's Office of Science, being named an ISI Foundation Fellow, recognition as one of the 100 Brilliant Women in AI Ethics, and receiving Northeastern University's Excellence in Research and Creative Activity Award. In 2023, she was awarded the Lagrange-CRT Foundation Prize. Prior to her current position, she was an associate professor at Rutgers University and a member of technical staff and principal investigator at Lawrence Livermore National Laboratory.

Selected publications

• Hypergraphs and simplicial complexes in focus: a roadmap for future research in higher-order interactions

  Journal of Physics Complexity · 2026-01-22 · 1 citations

  articleOpen access

  Abstract Higher-order interactions are increasingly being recognized as fundamental to our understanding of complex systems, networks, and the development of the next generation of AI algorithms. However, modeling higher-order interactions requires us to go beyond graphs and networks, which can only encode pairwise interactions, and so demands a new theory. Hypergraphs and simplicial complexes (also called higher-order networks), which arise as natural mathematical representations of higher-order complex systems, are therefore attracting increasing attention. The mathematics of higher-order networks is already providing important insights, yet many fundamental mathematical questions remain unsolved; for instance, in spectral graph theory, discrete topology, and higher-order network dynamics. This roadmap summarizes the scientific discussions that took place on these topics between pure mathematicians, theoretical physicists, computer and network scientists at the Newton Institute Satellite meeting on ‘Hypergraphs: Theory and Applications’. We survey the current state-of-the-art in higher-order network research, and propose some trajectories for future research, including in areas such as extremal and spectral hypergraph theory, discrete topology, higher-order dynamics, higher-order machine learning, and applications in the brain and social sciences.

  PublisherDOI

• When Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendations

  2025-06-23

  articleOpen accessSenior author

  We study the fairness of dimensionality reduction methods for recommendations.We focus on the fundamental method of principal component analysis (PCA), which identifies latent components and produces a low-rank approximation via the leading components while discarding the trailing components.Prior works have defined notions of "fair PCA"; however, these definitions do not answer the following question: why is PCA unfair?We identify two underlying popularity mechanisms that induce item unfairness in PCA.The first negatively impacts less popular items because less popular items rely on trailing latent components to recover their values.The second negatively impacts highly popular items, since the leading PCA components specialize in individual popular items instead of capturing similarities between items.To address these issues, we develop a polynomial-time algorithm, Item-Weighted PCA, that flexibly up-weights less popular items when optimizing for leading principal components.We theoretically show that PCA, in all cases, and Normalized PCA, in cases of block-diagonal matrices, are instances of Item-Weighted PCA.We empirically show that there exist datasets for which Item-Weighted PCA yields the optimal solution while the baselines do not.In contrast to past dimensionality reduction re-weighting techniques, Item-Weighted PCA solves a convex optimization problem and enforces a hard rank constraint.Our evaluations on real-world datasets show that Item-Weighted PCA not only mitigates both unfairness mechanisms, but also produces recommendations that outperform those of PCA baselines.

  PublisherOA PDFDOI

• A Survey on Hypergraph Mining: Patterns, Tools, and Generators

  ACM Computing Surveys · 2025-02-20 · 30 citations

  reviewOpen access

  Hypergraphs, which belong to the family of higher-order networks, are a natural and powerful choice for modeling group interactions in the real world. For example, when modeling collaboration networks, which may involve not just two but three or more people, the use of hypergraphs allows us to explore beyond pairwise (dyadic) patterns and capture groupwise (polyadic) patterns. The mathematical complexity of hypergraphs offers both opportunities and challenges for hypergraph mining. The goal of hypergraph mining is to find structural properties recurring in real-world hypergraphs across different domains, which we call patterns. To find patterns, we need tools. We divide hypergraph mining tools into three categories: (1) null models (which help test the significance of observed patterns), (2) structural elements (i.e., substructures in a hypergraph such as open and closed triangles), and (3) structural quantities (i.e., numerical tools for computing hypergraph patterns such as transitivity). There are also hypergraph generators, whose objective is to produce synthetic hypergraphs that are a faithful representation of real-world hypergraphs. In this survey, we provide a comprehensive overview of the current landscape of hypergraph mining, covering patterns, tools, and generators. We provide comprehensive taxonomies for each and offer in-depth discussions for future research on hypergraph mining.

  PublisherDOI

• Forecasting Faculty Placement from Patterns in Co-authorship Networks

  ArXiv.org · 2025-07-19

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Identifying and Upweighting Power-Niche Users to Mitigate Popularity Bias in Recommendations

  arXiv (Cornell University) · 2025-09-21

  preprintOpen access

  Recommender systems have been shown to exhibit popularity bias by over-recommending popular items and under-recommending relevant niche items. We seek to understand niche users in benchmark recommendation datasets as a step toward mitigating popularity bias. We find that, compared to mainstream users, niche-preferring users exhibit a longer-tailed activity-level distribution, indicating the existence of users who both prefer niche items and exhibit high activity levels on platforms. We partition users along two axes: (1) activity level ("power" vs. "light") and (2) item-popularity preference ("mainstream" vs. "niche"), and show that in three benchmark datasets, the number of power-niche users (high activity and niche preference) is statistically significantly larger than expected. We also find that interaction data from power-niche users is especially valuable for improving recommendations for not only niche but also mainstream users. In contrast, many existing popularity bias mitigation methods have focused on upweighting niche users regardless of activity level. Motivated by the value of power-niche user data, we propose PAIR (Popularity-and-Activity-Informed Reweighting), a framework for reweighting the Bayesian Personalized Ranking (BPR) loss that simultaneously reweights based on user activity level and item popularity, upweighting power-niche users the most. We instantiate the framework on both deep and shallow collaborative filtering models, and experiments on benchmark datasets show that PAIR reduces popularity bias and can increase overall performance. Although existing popularity-bias mitigation methods yield a trade-off between performance and bias, our results suggest that considering both user activity level and popularity preference leads to Pareto-dominant performance.

  PublisherOA PDFDOI

• Bypassing Skip-Gram Negative Sampling: Dimension Regularization as a More Efficient Alternative for Graph Embeddings

  2025-08-03

  articleOpen access

  A wide range of graph embedding objectives decompose into two components: one that enforces similarity, attracting the embeddings of nodes that are perceived as similar, and another that enforces dissimilarity, repelling the embeddings of nodes that are perceived as dissimilar. Without repulsion, the embeddings would collapse into trivial solutions. Skip-Gram Negative Sampling (SGNS) is a popular and efficient repulsion approach that prevents collapse by repelling each node from a sample of dissimilar nodes. In this work, we show that when repulsion is most needed and the embeddings approach collapse, SGNS node-wise repulsion is, in the aggregate, an approximate re-centering of the node embedding dimensions. Such dimension operations are more scalable than node operations and produce a simpler geometric interpretation of the repulsion. Our theoretical result establishes dimension regularization as an effective and more efficient, compared to skip-gram node contrast, approach to enforcing dissimilarity among embeddings of nodes. We use this result to propose a flexible algorithm augmentation framework that improves the scalability of any existing algorithm using SGNS. The framework prioritizes node attraction and replaces SGNS with dimension regularization. We instantiate this generic framework for LINE and node2vec and show that the augmented algorithms preserve downstream link-prediction performance while reducing GPU memory usage by up to 33.3% and training time by 23.4%. Moreover, we show that completely removing repulsion (a special case of our augmentation framework) in LINE reduces training time by 70.9% on average, while increasing link prediction performance, especially for graphs that are globally sparse but locally dense. Global sparsity slows down dimensional collapse, while local density ensures that node attraction brings the nodes near their neighbors. In general, however, repulsion is needed, and dimension regularization provides an efficient alternative to SGNS.

  PublisherOA PDFDOI

• Accelerated Discovery of Set Cover Solutions via Graph Neural Networks

  Lecture notes in computer science · 2025-01-01 · 1 citations

  book-chapter
  PublisherDOI

• Human-AI Coevolution (Abstract Reprint)

  2025-09-01

  article

  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. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices through online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often “unintended” systemic outcomes. This paper introduces human-AI coevolution as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., scientific, legal and socio-political.

  PublisherDOI

• Hypergraph Representations of scRNA-seq Data for Improved Clustering with Random Walks

  arXiv (Cornell University) · 2025-01-20

  preprintOpen accessSenior author

  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. However, this approach has several limitations: loss of higher-order information, inefficient data representation caused by converting a sparse dataset to a fully connected network, and overestimation of coexpression due to zero-inflation. To address these limitations, we propose conceptualizing scRNA-seq expression data as hypergraphs, which are generalized graphs in which the hyperedges can connect more than two vertices. In the context of scRNA-seq data, the hypergraph nodes represent cells and the edges represent genes. Each hyperedge connects all cells where its corresponding gene is actively expressed and records the expression of the gene across different cells. This hypergraph conceptualization enables us to explore multi-way relationships beyond the pairwise interactions in coexpression networks without loss of information. We propose two novel clustering methods: (1) the Dual-Importance Preference Hypergraph Walk (DIPHW) and (2) the Coexpression and Memory-Integrated Dual-Importance Preference Hypergraph Walk (CoMem-DIPHW). They outperform established methods on both simulated and real scRNA-seq datasets. The improvement brought by our proposed methods is especially significant when data modularity is weak. Furthermore, CoMem-DIPHW incorporates the gene coexpression network, cell coexpression network, and the cell-gene expression hypergraph from the single-cell abundance counts data altogether for embedding computation. This approach accounts for both the local level information from single-cell level gene expression and the global level information from the pairwise similarity in the two coexpression networks.

  PublisherOA PDFDOI

• Topology-driven negative sampling enhances generalizability in protein–protein interaction prediction

  Bioinformatics · 2025-04-07 · 2 citations

  articleOpen accessSenior author

  MOTIVATION: Unraveling the human interactome to uncover disease-specific patterns and discover drug targets hinges on accurate protein-protein interaction (PPI) predictions. However, challenges persist in machine learning (ML) models due to a scarcity of quality hard negative samples, shortcut learning, and limited generalizability to novel proteins. RESULTS: In this study, we introduce a novel approach for strategic sampling of protein-protein noninteractions (PPNIs) by leveraging higher-order network characteristics that capture the inherent complementarity-driven mechanisms of PPIs. Next, we introduce Unsupervised Pre-training of Node Attributes tuned for PPI (UPNA-PPI), a high throughput sequence-to-function ML pipeline, integrating unsupervised pre-training in protein representation learning with Topological PPNI (TPPNI) samples, capable of efficiently screening billions of interactions. By using our TPPNI in training the UPNA-PPI model, we improve PPI prediction generalizability and interpretability, particularly in identifying potential binding sites locations on amino acid sequences, strengthening the prioritization of screening assays and facilitating the transferability of ML predictions across protein families and homodimers. UPNA-PPI establishes the foundation for a fundamental negative sampling methodology in graph machine learning by integrating insights from network topology. AVAILABILITY AND IMPLEMENTATION: Code and UPNA-PPI predictions are freely available at https://github.com/alxndgb/UPNA-PPI.

  PublisherOA PDFDOI

Labs

• Rad LabPI

Awards & honors

• Outstanding Mentor Award from the US Department of Energy's…
• ISI Foundation Fellow (2019)
• One of the 100 Brilliant Women in AI Ethics (2021)
• Northeastern University's Excellence in Research and Creativ…
• Lagrange-CRT Foundation Prize (2023)