Max-Planck-Institut für Informatik
max planck institut
mpii logo Minerva of the Max Planck Society

MPI-INF or MPI-SWS or Local Campus Event Calendar

<< Previous Entry Next Entry >> New Event Entry Edit this Entry Login to DB (to update, delete)
What and Who
Title:Algorithmic Building Blocks for Relationship Analysis over Large Graphs
Speaker:Stephan Seufert
coming from:Max-Planck-Institut für Informatik - D5
Speakers Bio:
Event Type:Promotionskolloquium
Visibility:D1, D2, D3, D4, D5, SWS, RG1, MMCI
We use this to send out email in the morning.
Level:Public Audience
Date, Time and Location
Date:Monday, 20 April 2015
Duration:60 Minutes
Building:E1 4
Over the last decade, large-scale graph datasets with millions of vertices and edges have emerged in many diverse problem domains. Notable examples include online social networks, the Web graph, or knowledge graphs connecting semantically typed entities. An important problem in this setting lies in the analysis of the relationships between the contained vertices, in order to gain insights into the structure and dynamics of the modeled interactions.

In this work, we develop efficient and scalable algorithms for three important problems in relationship analysis and make the following contributions:

• We present the Ferrari index structure to quickly probe a graph for the existence of an (indirect) relationship between two designated query vertices, based on an adaptive compression of the transitive closure of the graph.

• In order to quickly assess the relationship strength for a given pair of vertices as well as computing the corresponding paths, we present the PathSketch index structure for the fast approximation of shortest paths in large graphs. Our work extends a previously proposed prototype in several ways, including efficient index construction, compact index size, and faster query processing.

• We present the Espresso algorithm for characterizing the relationship between two sets of entities in a knowledge graph. This algorithm is based on the identification of important events from the interaction history of the entities of interest. These events are subsequently expanded into coherent subgraphs, corresponding to characteristic topics describing the relationship.

We provide extensive experimental evaluations for each of the methods, demonstrating the efficiency of the individual algorithms as well as their usefulness for facilitating effective analysis of relationships in large graphs.
Name(s):Petra Schaaf
EMail:--email address not disclosed on the web
Video Broadcast
Video Broadcast:NoTo Location:
Tags, Category, Keywords and additional notes
Attachments, File(s):
Created:Petra Schaaf/AG5/MPII/DE, 03/11/2015 09:39 AM Last modified:Uwe Brahm/MPII/DE, 11/24/2016 04:13 PM
  • Petra Schaaf, 03/12/2015 10:50 AM
  • Petra Schaaf, 03/11/2015 01:50 PM -- Created document.