An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks

Network representation learning aims to learn low-dimensional, compressible, and distributed representational vectors of nodes in networks. Due to the expensive costs of obtaining label information of nodes in networks, many unsupervised network representation learning methods have been proposed, where random walk strategy is one of the wildly utilized approaches. However, the existing random walk based methods have some challenges, including: 1. The insufficiency of explaining what network knowledge in the walking path-samplings; 2. The adverse effects caused by the mixture of different information in networks; 3. The poor generality of the methods with hyper-parameters on different networks. This paper proposes an information-explainable random walk based unsupervised network representation learning framework named Probabilistic Accepted Walk (PAW) to obtain network representation from the perspective of the stationary distribution of networks. In the framework, we design two stationary distributions based on nodes’ self-information and local-information of networks to guide our proposed random walk strategy to learn representational vectors of networks through sampling paths of nodes. Numerous experimental results demonstrated that the PAW could obtain more expressive representation than the other six widely used unsupervised network representation learning baselines on four real-world networks in single-label and multi-label node classification tasks.

Two-Stage User Identification Based on User Topology Dynamic Community Clustering

In order to solve the problem of node information loss during user matching in the existing user identification method of fixed community across the social network based on user topological relationship, Two-Stage User Identification Based on User Topology Dynamic Community Clustering (UIUTDC) algorithm is proposed. Firstly, we perform community clustering on different social networks, calculate the similarity between different network communities, and screen out community pairs with greater similarity. Secondly, two-way marriage matching is carried out for users between pairs of communities with high similarity. Then, the dynamic community clustering was performed by resetting the different community clustering numbers. Finally, the iteration is repeated until no new matching user pairs are generated, or the set number of iterations is reached. Experiments conducted on real-world social networks Twitter-Foursquare datasets demonstrate that compared with the global user matching method and hidden label node method, the average accuracy of the proposed UIUTDC algorithm is improved by 33% and 26.8%, respectively. In the case of only user topology information, the proposed UIUTDC algorithm effectively improves the accuracy of identity recognition in practical applications.

Node Embedding over Temporal Graphs

In this work, we present a method for node embedding in temporal graphs. We propose an algorithm that learns the evolution of a temporal graph's nodes and edges over time and incorporates this dynamics in a temporal node embedding framework for different graph prediction tasks. We present a joint loss function that creates a temporal embedding of a node by learning to combine its historical temporal embeddings, such that it optimizes per given task (e.g., link prediction). The algorithm is initialized using static node embeddings, which are then aligned over the representations of a node at different time points, and eventually adapted for the given task in a joint optimization. We evaluate the effectiveness of our approach over a variety of temporal graphs for the two fundamental tasks of temporal link prediction and multi-label node classification, comparing to competitive baselines and algorithmic alternatives. Our algorithm shows performance improvements across many of the datasets and baselines and is found particularly effective for graphs that are less cohesive, with a lower clustering coefficient.

ZBLE: “Energy Efficient Zone-based Leader Election Multipath Routing Protocol for MANETs”

Inside the paper, an analysis of the representation of a zone-based multipath routing technology called Zone-Based Leader Election Energy Constrained AOMDV Routing Protocol (ZBLE) for MANETs has been presented. The primary purpose of the MANETs is to make system communication effective and efficient so that the quality of the network can be ensured. Consumption of energy in the MANETs has extended been a larger problem since the past. Movable devices present into the wireless environment are dependent on batteries and cannot fulfill the power supply due to limited power capacity. To address this problem, we have used zone-based technology which is designed by modifying the AOMDV protocol. Inside here, the demonstration about a zone-based system for the wireless network has been analyzed which is called Zone-Based Leader Election Energy Constrained AOMDV Routing Protocol (ZBLE). It has been implemented for energy efficient communication based on energy label, node tracking, and power analysis. It is a zone-based technology that works in keeping with the energy of multipath routing in mind. ZBLE protocols prolong the network's life by reducing balanced energy consumption between nodes. This protocol is compared to the traditional route protocol i.e. AODV and AOMDV, in which it has been found that the ZBLE protocol presents better results