Ego-centric Graph Pattern Census

@article{Moustafa2012EgocentricGP,
  title={Ego-centric Graph Pattern Census},
  author={Walaa Eldin M. Moustafa and Amol Deshpande and Lise Getoor},
  journal={2012 IEEE 28th International Conference on Data Engineering},
  year={2012},
  pages={234-245}
}
There is increasing interest in analyzing networks of all types including social, biological, sensor, computer, and transportation networks. Broadly speaking, we may be interested in global network-wide analysis (e.g., centrality analysis, community detection) where the properties of the entire network are of interest, or local ego-centric analysis where the focus is on studying the properties of nodes (egos) by analyzing their neighborhood sub graphs. In this paper we propose and study ego… 

Figures and Tables from this paper

An Automated System for Discovering Neighborhood Patterns in Ego Networks
TLDR
An automated system is developed in order to discover the occurrences of prototypical ego-centric patterns from data to provide a data-driven instrument to be used in behavioral sciences for graph interpretations.
EAGr: supporting continuous ego-centric aggregate queries over large dynamic graphs
TLDR
EAGr, a system for supporting large numbers of continuous neighborhood-based ("ego-centric") aggregate queries over large, highly dynamic, rapidly evolving graphs, and presents an optimal, polynomial-time algorithm for making the pre-computation decisions given an overlay graph.
DUKE: A Solution for Discovering Neighborhood Patterns in Ego Networks
TLDR
This work presents a novel solution that discovers occurrences of prototypical ’ego network’ patterns from social media and mobile-phone networks, to provide a data-driven instrument to be used in behavioral sciences for graph interpretations.
Central limit theorems for local network statistics
TLDR
This work derives the asymptotic joint distribution of rooted subgraph counts in inhomogeneous random graphs, a model which generalizes many popular statistical network models and enables a shift in the statistical analysis of large graphs, from estimating network summaries, to estimating models linking local network structure and vertex-specific covariates.
Towards Neighborhood Window Analytics over Large-Scale Graphs
TLDR
A novel index, Dense Block Index DBIndex, is developed to facilitate efficient processing of k-hop window queries and are superior over the state-of-the-art solution in terms of both scalability and efficiency.
Partial view selection for evolving social graphs
TLDR
This paper proposes deploying partial view instead of full snapshot construction and defines conditions that determine when a partial view can be used to evaluate a query, and proposes using a cache of partial views to reduce the query evaluation cost.
egoComp: A node-link-based technique for visual comparison of ego-networks
TLDR
To preserve the latent structure of ego-network and lay emphasis on intuitiveness, the design is node-link-based (radial tree layout) and uses a side-by-side method to compare ego-nets, and a novel storyflow-like graph layout to reveal the relationship of two ego-networks at the individual node level.
Graph Pattern Mining, Search and OLAP
TLDR
The existing studies are mostly focused on the multiple graphs scenario, but with some modifications, the mining methodology can be extended to the single graph scenario with limited modifications.
Finding the Needle in a Haystack: Entropy Guided Exploration of Very Large Graph Cubes
TLDR
This work utilizes information entropy measures in order to help the analyst navigate within the rich information contained in a graph cube, and proposes a graph analysis workflow that first suggests interesting cuboids from the exponential collection of aggregations that exist in the graph cube.
...
...

References

SHOWING 1-10 OF 49 REFERENCES
On graph query optimization in large networks
TLDR
The experimental studies demonstrate the effectiveness and scalability of SPath, which proves to be a more practical and efficient indexing method in addressing graph queries on large networks.
GADDI: distance index based subgraph matching in biological networks
TLDR
A novel distance measurement is proposed which reintroduces the idea of frequent substructures in a single large graph in a given large graph of thousands of vertices and the novel structure distance based approach (GADDI) is devised to efficiently find matches of the query graph.
Graph Indexing: Tree + Delta >= Graph
TLDR
This study verifies that (Tree+Δ) is a better choice than graph for indexing purpose, denoted (Tree-Δ ≥Graph), to address the graph containment query problem and achieves an order of magnitude better performance in index construction.
Subgraphs and network motifs in geometric networks.
  • S. Itzkovitz, U. Alon
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2005
TLDR
Geometric network models, in which nodes are arranged on a lattice and edges are formed with a probability that decays with the distance between nodes, are analyzed and it is found that network motifs in many real-world networks are not captured solely by these geometric models.
Adding regular expressions to graph reachability and pattern queries
TLDR
A class of reachability queries and a class of graph patterns, in which an edge is specified with a regular expression of a certain form, expressing the connectivity of a data graph via edges of various types are proposed.
Declarative analysis of noisy information networks
TLDR
This paper identifies a set of primitives to support the extraction and inference of a network from observational data, and describes a framework that enables a network analyst to easily implement and combine new extraction and analysis techniques, and efficiently apply them to large observation networks.
Biological network comparison using graphlet degree distribution
TLDR
Almost all of the 14 eukaryotic PPI networks, including human, resulting from various high-throughput experimental techniques, as well as from curated databases, are better modeled by geometric random graphs than by Erdös-Rény, random scale-free, or Barabási-Albert scale- free networks.
DistanceJoin: Pattern Match Query In a Large Graph Database
TLDR
This paper first transforms the vertices into points in a vector space via graph embedding techniques, coverting a pattern match query into a distance-based multi-way join problem over the converted vector space, and proposes several pruning strategies and a join order selection method to process join processing efficiently.
On compressing social networks
TLDR
This work proposes simple combinatorial formulations that encapsulate efficient compressibility of graphs and shows that some of the problems are NP-hard yet admit effective heuristics, some of which can exploit properties of social networks such as link reciprocity.
Explicit Graphs in a Functional Model for Spatial Databases
TLDR
A new data model and query language that especially supports graph structures is defined, which leads to powerful modeling and querying capabilities for spatial databases, in particular for spatially embedded networks such as highways, rivers, public transport, and so forth.
...
...