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ICPR
2002
IEEE
16 years 8 months ago
Relational Graph Labelling Using Learning Techniques and Markov Random Fields
This paper introduces an approach for handling complex labelling problems driven by local constraints. The purpose is illustrated by two applications: detection of the road networ...
Denis Rivière, Jean-Francois Mangin, Jean-M...
KDD
2009
ACM
204views Data Mining» more  KDD 2009»
16 years 8 months ago
DOULION: counting triangles in massive graphs with a coin
Counting the number of triangles in a graph is a beautiful algorithmic problem which has gained importance over the last years due to its significant role in complex network analy...
Charalampos E. Tsourakakis, U. Kang, Gary L. Mille...
OSDI
2004
ACM
16 years 8 months ago
FFPF: Fairly Fast Packet Filters
FFPF is a network monitoring framework designed for three things: speed (handling high link rates), scalability (ability to handle multiple applications) and flexibility. Multiple...
Herbert Bos, Willem de Bruijn, Mihai-Lucian Criste...
DCC
2006
IEEE
16 years 7 months ago
Joint Source-Channel Decoding of Multiple Description Quantized Markov Sequences
This paper proposes a framework for joint source-channel decoding of Markov sequences that are coded by a fixed-rate multiple description quantizer (MDQ), and transmitted via a lo...
Xiaolin Wu, Xiaohan Wang
ICNP
2009
IEEE
16 years 2 months ago
Better by a HAIR: Hardware-Amenable Internet Routing
—Routing protocols are implemented in the form of software running on a general-purpose microprocessor. However, conventional software-based router architectures face significan...
Firat Kiyak, Brent Mochizuki, Eric Keller, Matthew...