A CPU-GPU local search heuristic for the maximum weight clique problem on massive graphs

2018 ◽  
Vol 90 ◽  
pp. 232-248 ◽  
Author(s):  
Bruno Nogueira ◽  
Rian G.S. Pinheiro
Keyword(s):  
Local Search ◽  
Maximum Weight ◽  
Search Heuristic ◽  
Local Search Heuristic ◽  
Massive Graphs ◽  
Maximum Weight Clique ◽  
Clique Problem

scholarly journals A Complementary Pivoting Approach to the Maximum Weight Clique Problem

2002 ◽  
Vol 12 (4) ◽  
pp. 928-948 ◽  
Author(s):  
Alessio Massaro ◽  
Marcello Pelillo ◽  
Immanuel M. Bomze
Keyword(s):  
Maximum Weight ◽  
Complementary Pivoting ◽  
Maximum Weight Clique ◽  
Clique Problem

scholarly journals Deanonymizing Social Networks Using Structural Information

2019 ◽  
Author(s):  
Ioannis Caragiannis ◽  
Evanthia Tsitsoka
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Local Search ◽  
Structural Information ◽  
Initial Solution ◽  
Bipartite Matching ◽  
Search Heuristic ◽  
Local Search Heuristic ◽  
Anonymous Network ◽  
Random Models

We study the following fundamental graph problem that models the important task of deanonymizing social networks. We are given a graph representing an eponymous social network and another graph, representing an anonymous social network, which has been produced by the original one after removing some of its nodes and adding some noise on the links. Our objective is to correctly associate as many nodes of the anonymous network as possible to their corresponding node in the eponymous network. We present two algorithms that attack the problem by exploiting only the structure of the two graphs. The first one exploits bipartite matching computations and is relatively fast. The second one is a local search heuristic which can use the outcome of our first algorithm as an initial solution and further improve it. We have applied our algorithms on inputs that have been produced by well-known random models for the generation of social networks as well as on inputs that use real social networks. Our algorithms can tolerate noise at the level of up to 10%. Interestingly, our results provide further evidence to which graph generation models are most suitable for modeling social networks and distinguish them from unrealistic ones.

Sign in / Sign up

Export Citation Format

Share Document