Multiplicative Attribute Graph Model of Real-World Networks

Multiplicative Attribute Graph Model of Real-World Networks

Large scale real-world network data such as social and information networks are ubiquitous. Thestudy of such social and information networks seeks to find patterns and explain their emergence throughtractable models. In most networks, and especially in social networks, nodes have a rich set of attributes(e.g., age, gender) associated with them.Here we present a model that we...

Modeling Social Networks with Node Attributes using the Multiplicative Attribute Graph Model

Networks arising from social, technological and natural domains exhibit rich connectivity patterns and nodes in such networks are often labeled with attributes or features. We address the question of modeling the structure of networks where nodes have attribute information. We present a Multiplicative Attribute Graph (MAG) model that considers nodes with categorical attributes and models the pr...

Applications of Temporal Graph Metrics to Real-World Networks

Real world networks exhibit rich temporal information: friends are added and removed over time in online social networks; the seasons dictate the predatorprey relationship in food webs; and the propagation of a virus depends on the network of human contacts throughout the day. Recent studies have demonstrated that static network analysis is perhaps unsuitable in the study of real world network ...

The Maximum Number of Edges in a Strongly Multiplicative Graph

A Multiplicative Model for Learning Distributed Text-Based Attribute Representations

In this paper we propose a general framework for learning distributed representations of attributes: characteristics of text whose representations can be jointly learned with word embeddings. Attributes can correspond to a wide variety of concepts, such as document indicators (to learn sentence vectors), language indicators (to learn distributed language representations), meta-data and side inf...