222 citations · 762 across the 46 of their papers we have counts for
17 papers · 1 filter
Heterogeneous Graphlets
Ryan A. Rossi, Nesreen K. Ahmed, Aldo Carranza +4
In this paper, we introduce a generalization of graphlets to heterogeneous networks called typed graphlets. Informally, typed graphlets are small typed induced subgraphs. Typed gra…
On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications
Ryan A. Rossi, Di Jin, Sungchul Kim +3
Structural roles define sets of structurally similar nodes that are more similar to nodes inside the set than outside, whereas communities define sets of nodes with more connection…
Adaptive Shrinkage Estimation for Streaming Graphs
Nesreen K. Ahmed, Nick Duffield
Networks are a natural representation of complex systems across the sciences, and higher-order dependencies are central to the understanding and modeling of these systems. However,…
Linear-time Hierarchical Community Detection
Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh +1
Community detection in graphs has many important and fundamental applications including in distributed systems, compression, image segmentation, divide-and-conquer graph algorithms…
Heterogeneous Network Motifs
Ryan A. Rossi, Nesreen K. Ahmed, Aldo Carranza +4
Many real-world applications give rise to large heterogeneous networks where nodes and edges can be of any arbitrary type (e.g., user, web page, location). Special cases of such he…
Predicting Graph Categories from Structural Properties
James P. Canning, Emma E. Ingram, Sammantha Nowak-Wolff +5
This paper has been withdrawn from arXiv.org due to a disagreement among the authors related to several peer-review comments received prior to submission on arXiv.org. Even though…
Sampling for Approximate Bipartite Network Projection
Nesreen K. Ahmed, Nick Duffield, Liangzhen Xia
Bipartite networks manifest as a stream of edges that represent transactions, e.g., purchases by retail customers. Many machine learning applications employ neighborhood-based meas…
Network Classification and Categorization
James P. Canning, Emma E. Ingram, Sammantha Nowak-Wolff +5
To the best of our knowledge, this paper presents the first large-scale study that tests whether network categories (e.g., social networks vs. web graphs) are distinguishable from…
On Sampling from Massive Graph Streams
Nesreen K. Ahmed, Nick Duffield, Theodore Willke +1
We propose Graph Priority Sampling (GPS), a new paradigm for order-based reservoir sampling from massive streams of graph edges. GPS provides a general way to weight edge sampling…
Estimation of Graphlet Statistics
Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed
Graphlets are induced subgraphs of a large network and are important for understanding and modeling complex networks. Despite their practical importance, graphlets have been severe…
Graphlet Decomposition: Framework, Algorithms, and Applications
Nesreen K. Ahmed, Jennifer Neville, Ryan A. Rossi +2
From social science to biology, numerous applications often rely on graphlets for intuitive and meaningful characterization of networks at both the global macro-level as well as th…
A Web-based Interactive Visual Graph Analytics Platform
Nesreen K. Ahmed, Ryan A. Rossi
This paper proposes a web-based visual graph analytics platform for interactive graph mining, visualization, and real-time exploration of networks. GraphVis is fast, intuitive, and…
Role Discovery in Networks
Ryan A. Rossi, Nesreen K. Ahmed
Roles represent node-level connectivity patterns such as star-center, star-edge nodes, near-cliques or nodes that act as bridges to different regions of the graph. Intuitively, two…
Graph Sample and Hold: A Framework for Big-Graph Analytics
Nesreen K. Ahmed, Nick Duffield, Jennifer Neville +1
Sampling is a standard approach in big-graph analytics; the goal is to efficiently estimate the graph properties by consulting a sample of the whole population. A perfect sample is…
Learning the Latent State Space of Time-Varying Graphs
Nesreen K. Ahmed, Christopher Cole, Jennifer Neville
From social networks to Internet applications, a wide variety of electronic communication tools are producing streams of graph data; where the nodes represent users and the edges r…
Coloring Large Complex Networks
Ryan A. Rossi, Nesreen K. Ahmed
Given a large social or information network, how can we partition the vertices into sets (i.e., colors) such that no two vertices linked by an edge are in the same set while minimi…
Network Sampling: From Static to Streaming Graphs
Nesreen K. Ahmed, Jennifer Neville, Ramana Kompella
Network sampling is integral to the analysis of social, information, and biological networks. Since many real-world networks are massive in size, continuously evolving, and/or dist…