Network (or graph) embedding is the task to map the nodes of a graph to a lower dimensional vector space, such that it preserves the graph properties and facilitates the downstream network mining tasks. Real world networks often come with (community) outlier nodes, which behave differently from the regular nodes of the community. These outlier nodes can affect the embedding of the regular nodes, if not handled carefully. In this paper, we propose a novel unsupervised graph embedding approach (called DMGD) which integrates outlier and community detection with node embedding. We extend the idea of deep support vector data description to the framework of graph embedding when there are multiple communities present in the given network, and an outlier is characterized relative to its community. We also show the theoretical bounds on the number of outliers detected by DMGD. Our formulation boils down to an interesting minimax game between the outliers, community assignments and the node embedding function. We also propose an efficient algorithm to solve this optimization framework. Experimental results on both synthetic and real world networks show the merit of our approach compared to state-of-the-arts.

Attributed network embedding is the task to learn a lower dimensional vector representation of the nodes of an attributed network, which can be used further for downstream network mining tasks. Nodes in a network exhibit community structure and most of the network embedding algorithms work well when the nodes, along with their attributes, adhere to the community structure of the network. But real life networks come with community outlier nodes, which deviate significantly in terms of their link structure or attribute similarities from the other nodes of the community they belong to. These outlier nodes, if not processed carefully, can even affect the embeddings of the other nodes in the network. Thus, a node embedding framework for dealing with both the link structure and attributes in the presence of outliers in an unsupervised setting is practically important. In this work, we propose a deep unsupervised autoencoders based solution which minimizes the effect of outlier nodes while generating the network embedding. We use both stochastic gradient descent and closed form updates for faster optimization of the network parameters. We further explore the role of adversarial learning for this task, and propose a second unsupervised deep model which learns by discriminating the structure and the attribute based embeddings of the network and minimizes the effect of outliers in a coupled way. Our experiments show the merit of these deep models to detect outliers and also the superiority of the generated network embeddings for different downstream mining tasks. To the best of our knowledge, these are the first unsupervised non linear approaches that reduce the effect of the outlier nodes while generating Network Embedding.

This paper describes the use of two dimensional (2-D) laser scanner for locating badminton shuttlecock in real playing environment. It proposes a method to predict the end point of shuttlecock trajectory. The system is designed using two 2-D laser scanners to locate shuttlecock in midst of air in its trajectory. It helps to calculate shuttlecock's speed, orientation and hence, to predict an end point of its trajectory. This system acts as an intelligent feedback system to a badminton playing robot. The badminton playing robot requires enhanced and deterministic shuttlecock detection system for its accurate operations. The shuttlecock detection system can be implemented in designing of such badminton playing robots making the badminton sport more advanced as robots can be used to assist players in training programs. The paper deals with simulation and real experimental results obtained by two 2-D laser scanners to perform complex task of shuttlecock trajectory prediction. The physical implementation ensures minimum computational latency over traditional camera based shuttlecock detection methods. On field trials involved two scanners to locate shuttlecock at discrete time interval and promising results are obtained indicating such detection system with suitable modifications can be employed in shuttlecock trajectory prediction.