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کامپیوتر و شبکه::
نرم افزار مطالعه شبکه خودمحور(EgoNet)
(2010) specify features of egonet such as degree of the ego, its total weight, number of edges in the egonet, and principal eigenvalue of the weighted adja- cency matrix of the egonet.
An egonet consists of a central node called ego and a set of neighbors to which the ego is directly connected to, plus the edges among them.
(2010) extend the feature base and propose an algorithm called ReFeX (Recursive Feature eXtraction) that recursively aggregates node-based features with neighborhood or egonet- based features and yields regional features by capturing "behavioral" information.
Oddball uses the features of egonet such as degree of the ego, its total weight, number of edges in the egonet, and principal eigenvalue of the weighted adjacency matrix of the egonet to detect anomalies.
The egonet features are then examined in pairs and different power- law rules are defined among the features.
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