• Publications
  • Influence
REGAL: Representation Learning-based Graph Alignment
tl;dr
We propose REGAL (REpresentation learning-based Graph ALignment), a framework that leverages the power of automatically-learned node representations to match nodes across different graphs. Expand
  • 59
  • 10
  • Open Access
Graph Summarization Methods and Applications
tl;dr
Graph summarization is a problem in graph mining with connections to relational data management and visualization. Expand
  • 67
  • 2
  • Open Access
Graph Summarization Methods and Applications: A Survey.
tl;dr
This survey is a structured, comprehensive overview of the state-of-the-art methods for summarizing graph data. Expand
  • 33
  • 1
A Graph Summarization: A Survey
tl;dr
We present the state-of-the-art methods for summarizing interconnected data, otherwise known as graphs. Expand
  • 30
  • Open Access
Scalable Hashing-Based Network Discovery
tl;dr
We propose ABC-LSH, a 3-step approach that approximates time series, hashes them via window sampling, and builds a graph based on the results of hashing. Expand
  • 16
  • Open Access
Reducing large graphs to small supergraphs: a unified approach
tl;dr
We propose CONditional Diversified Network Summarization (CondeNSe), a Minimum Description Length-based method that summarizes a given graph with approximate “supergraphs" conditioned on a set of diverse, predefined structural patterns. Expand
  • 6
  • Open Access
Career Transitions and Trajectories: A Case Study in Computing
tl;dr
We analyze several decades of post-PhD computing careers using a large new dataset rich with professional information, and propose a versatile career network model, R 3 , that captures temporal career dynamics. Expand
  • 8
  • Open Access
Personalized Knowledge Graph Summarization: From the Cloud to Your Pocket
tl;dr
We propose a new problem called personalized knowledge graph summarization. Expand
  • 5
  • Open Access
Smart Roles: Inferring Professional Roles in Email Networks
tl;dr
We propose EMBER, or EMBedding Email-based Roles, which finds email-centric embeddings of network nodes to be used in professional role inference tasks. Expand
  • 3
  • Open Access
Distribution of Node Embeddings as Multiresolution Features for Graphs
tl;dr
We propose Randomized Grid Mapping (RGM), a fast-to-compute feature map that represents a graph via the distribution of its node embeddings in feature space. Expand
  • 5
  • Open Access