HARSH KARA

A computer cluster is a group of linked computers, working together closely so that in many respects they form a single computer. The components of a cluster are commonly, but not always, connected to each other through fast local area networks. Clusters are usually deployed to improve performance and/or availability over that provided by a single computer, while typically being much more cost-effective than single computers of comparable speed or availability. The major objective in the cluster is utilizing a group of processing nodes so as to complete the assigned job in a minimum amount of time by working cooperatively. The main and vital strategy to achieve such objective is by transferring the extra loads from busy nodes to idle nodes. Network File System is a distributed file system protocol permit a client on a client computer to right of entry files over a network much like home storage is admittance. NFS, like many other protocols, builds on the Open Network Computing Remot...

Network representation learning (also known as information network embedding) has been the central piece of research in social and information network analysis for the last couple of years. An information network can be viewed as a linked structure of a set of entities. A set of linked web pages and documents, a set of users in a social network are common examples of information network. Network embedding learns low dimensional representations of the nodes, which can further be used for downstream network mining applications such as community detection or node clustering. Information network representation techniques traditionally use only the link structure of the network. But in real world networks, nodes come with additional content such as textual descriptions or associated images. This content is semantically correlated with the network structure and hence using the content along with the topological structure of the network can facilitate the overall network representation. In...

Network representation learning (also known as Graph embedding) is a technique to map the nodes of a network to a lower dimensional vector space. Random walk based representation techniques are found to be efficient as they can easily preserve different orders of proximities between the nodes in the embedding space. Most of the social networks now-a-days have some content (or attributes) associated with each node. These attributes can provide complementary information along with the link structure of the network. But in a real life network, the information carried by the link structure and that by the attributes vary significantly over the nodes. Most of the existing unsupervised attributed network embedding algorithms do not distinguish between the link structure and the attributes of a node depending on their informativeness. In this work, we propose an unsupervised node embedding technique that exploits both the structure and attributes by intelligently prioritizing one of them, ...

Network embedding has been a central topic of information network mining in the last couple of years. Network embedding learns a compact lower dimensional vector representation for each node of the network, and uses this lower dimensional representation for different machine learning and mining applications. The existing methods for network embedding consider mainly the structure of the network. But many real world networks also contain rich textual or other type of content associated with each node, which can help to understand the underlying semantics of the network. It is not straightforward to integrate the content of each node in the state-of-the-art network embedding methods. In this work, we propose a nonnegative matrix factorization based optimization framework, namely FSCNMF which considers both the network structure and the content of the nodes while learning a lower dimensional vector representation of each node in the network. Our approach systematically exploits the con...