n-Level Graph Partitioning
نویسندگان
چکیده
We present a multi-level graph partitioning algorithm based on the extreme idea tocontract only a single edge on each level of the hierarchy. This obviates the need for amatching algorithm and promises very good partitioning quality since there are veryfew changes between two levels. Using an efficient data structure and new flexible waysto break local search improvements early, we obtain an algorithm that scales to largeinputs and produces the best known partitioning results for many inputs. For example,in Walshaw’s well known benchmark tables we achieve 155 improvements dominatingthe entries for large graphs.
منابع مشابه
Two Randomized Algorithms for Multichip Partitioning Under Multiple Constraints
Graph partitioning is used in high-level synthesis systems in at least two contexts: (1) To facilitate performance estimation; (2) To partition the design into multiple components as is the case in MCM synthesis. In both cases, the graph being partitioned represents a design at the register level or a higher-level such as the behavior level or process level. Graph partitioning has been studied ...
متن کاملFeature Level Fusion of Face and Palmprint Biometrics
This paper presents a feature level fusion approach which uses the improved K-medoids clustering algorithm and isomorphic graph for face and palmprint biometrics. Partitioning around medoids (PAM) algorithm is used to partition the set of n invariant feature points of the face and palmprint images into k clusters. By partitioning the face and palmprint images with scale invariant features SIFT ...
متن کاملk-way Hypergraph Partitioning via n-Level Recursive Bisection
We develop a multilevel algorithm for hypergraph partitioning that contracts the vertices one at a time. Using several caching and lazy-evaluation techniques during coarsening and refinement, we reduce the running time by up to two-orders of magnitude compared to a naive n-level algorithm that would be adequate for ordinary graph partitioning. The overall performance is even better than the wid...
متن کاملMultibiometrics Feature Level Fusion by Graph Clustering
This paper presents a feature level fusion approach which uses the improved K-medoids clustering algorithm and isomorphic graph for face and palmprint biometrics. Partitioning around medoids (PAM) algorithm is used to partition the set of n invariant feature points of the face and palmprint images into k clusters. By partitioning face and palmprint images with scale invariant features SIFT poin...
متن کاملGraph partitioning induced phase transitions.
We study the percolation properties of graph partitioning on random regular graphs with N vertices of degree k. Optimal graph partitioning is directly related to optimal attack and immunization of complex networks. We find that for any partitioning process (even if nonoptimal) that partitions the graph into essentially equal sized connected components (clusters), the system undergoes a percolat...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2010