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Role Discovery in Networks
Abstract:
Roles represent node-level connectivity patterns such as star-center, star-
edge nodes, near-cliques or nodes that act as bridges to different regions of
the graph. Intuitively, two nodes belong to the same role if they are
structurally similar. Roles have been mainly of interest to sociologists, but
more recently, roles have become increasingly useful in other domains.
Traditionally, the notion of roles were defined based on graph equivalences
such as structural, regular, and stochastic equivalences. We briefly revisit
these early notions and instead propose a more general formulation of
roles based on the similarity of a feature representation (in contrast to the
graph representation). This leads us to propose a taxonomy of three
general classes of techniques for discovering roles that includes (i) graph-
based roles, (ii) feature-based roles, and (iii) hybrid roles. We also propose
a flexible framework for discovering roles using the notion of similarity on
a feature-based representation. The framework consists of two
fundamental components: (a) role feature construction and (b) role
assignment using the learned feature representation. We discuss the
different possibilities for discovering feature-based roles and the tradeoffs
of the many techniques for computing them. Finally, we discuss potential
applications and future directions and challenges.
Existing System:
An anomaly in this setting might be a node that doesn’t fit any of the roles
(normal structural patterns) or it may also be defined as a role that deviates
from the common/normal roles, so that any node assigned to this unusual
role would be anomalous.
Another use might be in online advertising campaigns for online social
networks (Facebook, Groupon, Yelp) where ads could be customized based
on the users’ roles in the network.
Despite the various applications, the task of role discovery has only
received a limited amount of attention (e.g., compared to the task of
community partitioning
Proposed System:
Role discovery, informally speaking, is any process that takes a graph and
picks out sets of nodes with similar structural patterns. Intuitively, two
nodes belong to the same role if they are structurally similar. Roles of a
graph represent the main node-level connectivity patterns such as star-
center/edge nodes, peripheral nodes, near-clique nodes, bridge nodes that
connect different regions of the graph, among many other types of
connectivity patterns.
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• RAM : 256 Mb.
Software Requirements:
• Operating system : - Windows XP.
• Front End : - JSP
• Back End : - SQL Server
Software Requirements:
• Operating system : - Windows XP.
• Front End : - .Net
• Back End : - SQL Server

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Role discovery in networks

  • 1. Role Discovery in Networks Abstract: Roles represent node-level connectivity patterns such as star-center, star- edge nodes, near-cliques or nodes that act as bridges to different regions of the graph. Intuitively, two nodes belong to the same role if they are structurally similar. Roles have been mainly of interest to sociologists, but more recently, roles have become increasingly useful in other domains. Traditionally, the notion of roles were defined based on graph equivalences such as structural, regular, and stochastic equivalences. We briefly revisit these early notions and instead propose a more general formulation of roles based on the similarity of a feature representation (in contrast to the graph representation). This leads us to propose a taxonomy of three general classes of techniques for discovering roles that includes (i) graph- based roles, (ii) feature-based roles, and (iii) hybrid roles. We also propose a flexible framework for discovering roles using the notion of similarity on a feature-based representation. The framework consists of two fundamental components: (a) role feature construction and (b) role assignment using the learned feature representation. We discuss the different possibilities for discovering feature-based roles and the tradeoffs of the many techniques for computing them. Finally, we discuss potential applications and future directions and challenges.
  • 2. Existing System: An anomaly in this setting might be a node that doesn’t fit any of the roles (normal structural patterns) or it may also be defined as a role that deviates from the common/normal roles, so that any node assigned to this unusual role would be anomalous. Another use might be in online advertising campaigns for online social networks (Facebook, Groupon, Yelp) where ads could be customized based on the users’ roles in the network. Despite the various applications, the task of role discovery has only received a limited amount of attention (e.g., compared to the task of community partitioning Proposed System: Role discovery, informally speaking, is any process that takes a graph and picks out sets of nodes with similar structural patterns. Intuitively, two nodes belong to the same role if they are structurally similar. Roles of a graph represent the main node-level connectivity patterns such as star- center/edge nodes, peripheral nodes, near-clique nodes, bridge nodes that connect different regions of the graph, among many other types of connectivity patterns.
  • 3. Hardware Requirements: • System : Pentium IV 2.4 GHz. • Hard Disk : 40 GB. • Floppy Drive : 1.44 Mb. • Monitor : 15 VGA Colour. • Mouse : Logitech. • RAM : 256 Mb. Software Requirements: • Operating system : - Windows XP. • Front End : - JSP • Back End : - SQL Server Software Requirements: • Operating system : - Windows XP. • Front End : - .Net • Back End : - SQL Server