Scattering Networks for Hybrid Representation Learning

NodeTrix: Hybrid Representation for Analyzing Social Networks

The need to visualize large social networks is growing as hardware capabilities make analyzing large networks feasible and many new data sets become available. Unfortunately, the visualizations in existing systems do not satisfactorily answer the basic dilemma of being readable both for the global structure of the network and also for detailed analysis of local communities. To address this prob...

Fuzzy Bayesian Networks - A General Formalism for Representation, Inference and Learning with Hybrid Bayesian Networks

Representation Learning for Scale-free Networks

Network embedding aims to learn the low-dimensional representations of vertexes in a network, while structure and inherent properties of the network is preserved. Existing network embedding works primarily focus on preserving the microscopic structure, such as the firstand second-order proximity of vertexes, while the macroscopic scale-free property is largely ignored. Scale-free property depic...

HARP: Hierarchical Representation Learning for Networks

We present HARP, a novel method for learning low dimensional embeddings of a graph’s nodes which preserves higher-order structural features. Our proposed method achieves this by compressing the input graph prior to embedding it, e ectively avoiding troublesome embedding con gurations (i.e. local minima) which can pose problems to non-convex optimization. HARP works by nding a smaller graph whic...

HYREP: A Hybrid Low-Power Protocol for Wireless Sensor Networks

In this paper, a new hybrid routing protocol is presented for low power Wireless Sensor Networks (WSNs). The new system uses an integrated piezoelectric energy harvester to increase the network lifetime. Power dissipation is one of the most important factors affecting lifetime of a WSN. An innovative cluster head selection technique using Cuckoo optimization algorithm has been used in the desig...