The Web as a graph
By Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, D. Sivakumar, Andrew Tomkins, Eli Upfal. ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, 2000
368 citations recorded by the source corpus.
Subfields: Information Systems, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/24f0c3f080b15708c9c38341f65c3a0b8fab51f2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
- Christos Faloutsos
- Jennifer Golbeck
- Symeon Papadopoulos
- Tamás Horváth
- Stefan Wrobel
- Thomas Gärtner
- J. Hendler
- Yiannis Kompatsiaris
In Statistical and Nonlinear Physics
- Christos Faloutsos
- Symeon Papadopoulos
- J. Hendler
- Jennifer Golbeck
- Yiannis Kompatsiaris
- Bijan Parsia
- Georgos Siganos
- Petros Faloutsos
Citations in the corpus
- Power laws and the AS-level internet topology (2003, background): “In addition, the topology of the World Wide Web [4], [28] can be described by power laws.”
- Cyclic pattern kernels for predictive graph mining (2004, background): “While in most of the web mining applications instances are vertices of the single massive web graph [21]; in other application domains each instance can be a graph .”
- Random Dot Product Graph Models for Social Networks (2007, methodology): “In a similar vein, the copying model [7, 8], also attempts to model the growth process of a complex networks.”
- ANF: a fast and scalable tool for data mining in massive graphs (2002, background): “State-of-the-art approaches to understanding/characterizing the Internet and the Web very often make use of neighbourhood information [3, 13, 1, 20].”
- The Power Grid as a Complex Network: a Survey (2011, background): “…has been used in many different fields of knowledge, from biology [7] to chemistry [8], from linguistics to social sciences [9], from telephone call patterns [10] to computer networks [11] and web [12, 13] to virus spreading [14, 15, 16] to logistics [17, 18, 19] and also inter-banking systems [20].”
- Community detection in Social Media (2012, background): “The most profound Web network is the Web itself comprising billions of pages as vertices and their hyperlinks to each other as edges (Kumar et al. 2000).”
- Trust Networks on the Semantic Web (2003, methodology): “Other work has used this structure for classification [9] and community discovery [15].”