Neural‑Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding

Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years. Existing works have demonstrated that vertex representation learned through an embedding method provides superior performance in many real-world applications, such as node classification, link prediction, and community detection. However, most of the existing methods for network embedding only utilize topological information of a vertex, ignoring a rich set of nodal attributes (such as user profiles of an online social network, or textual contents of a citation network), which is abundant in all real-life networks. A joint network embedding that takes into account both attributional and relational information entails a complete network information and could further enrich the learned vector representations. In this work, we present Neural-Brane, a novel Neural Bayesian Personalized Ranking based Attributed Network Embedding. For a given network, Neural-Brane extracts latent feature representation of its vertices using a designed neural network model that unifies network topological information and nodal attributes. Besides, it utilizes Bayesian personalized ranking objective, which exploits the proximity ordering between a similar node pair and a dissimilar node pair. We evaluate the quality of vertex embedding produced by Neural-Brane by solving the node classification and clustering tasks on four real-world datasets. Experimental results demonstrate the superiority of our proposed method over the state-of-the-art existing methods

Analysis and implementation Of Data mining techniques using Naive-Bayes Classifier and Neural Networks

Taking wise career decision is so crucial for anybody for sure. In modern days there are excellent decision support tools like data mining tools for the people to make right decisions. This paper is an attempt to help the prospective students to make wise career decisions using technologies like data mining. In India technical manpower analysis is carried out by an organization named NTMIS (National Technical Manpower Information System), established in 1983-84 by India's Ministry of Educatio

Sampling networks by nodal attributes

In a social network individuals or nodes connect to other nodes by choosing one of the channels of communication at a time to re-establish the existing social links. Since available data sets are usually restricted to a limited number of channels or layers, these autonomous decision making processes by the nodes constitute the sampling of a multiplex network leading to just one (though very important) example of sampling bias caused by the behavior of the nodes. We develop a general setting to get insight and understand the class of network sampling models, where the probability of sampling a link in the original network depends on the attributes $h$ of its adjacent nodes. Assuming that the nodal attributes are independently drawn from an arbitrary distribution $\rho(h)$ and that the sampling probability $r(h_i , h_j)$ for a link $ij$ of nodal attributes $h_i$ and $h_j$ is also arbitrary, we derive exact analytic expressions of the sampled network for such network characteristics as the degree distribution, degree correlation, and clustering spectrum. The properties of the sampled network turn out to be sums of quantities for the original network topology weighted by the factors stemming from the sampling. Based on our analysis, we find that the sampled network may have sampling-induced network properties that are absent in the original network, which implies the potential risk of a naive generalization of the results of the sample to the entire original network. We also consider the case, when neighboring nodes have correlated attributes to show how to generalize our formalism for such sampling bias and we get good agreement between the analytic results and the numerical simulations.Comment: 11 pages, 5 figure

Re-Spatializing Gangs in the United States: An Analysis of Macro- and Micro-Level Network Structures

Despite the significant contributions from location-based gang studies, the network structure of gangs beyond localized settings remains a neglected but important area of research to better understand the national security implications of gang interconnectivity. The purpose of this dissertation is to examine the network structure of gangs at the macro- and micro-level using social network analysis. At the macro-level, some gangs have formed national alliances in perpetuity with their goals and objectives. In order to study gangs at the macro-level, this research uses open-source data to construct an adjacency matrix of gang alliances and rivalries to map the relationships between gangs and analyze their network centrality across multiple metrics. The results suggest that native gangs are highly influential when compared to immigrant gangs. Some immigrant gangs, however, derive influence by “bridging” the gap between rival gangs. Mexican Drug Trafficking Organizations (MDTOs) play a similar role and feature prominently in the gang network. Moreover, removing MDTOs changes the network structure in favor of ideologically-motivated gangs over profit-oriented gangs. Critics deride macro-level approaches to studying gangs for their lack of national cohesion. In response, this research includes a micro-level analysis of gang member connections by mining Twitter data to analyze the geospatial distribution of gang members and, by proxy, gangs, using an exponential random graph model (ERGM) to test location homophily and better understand the extent to which gang members are localized. The findings show a positive correlation between location and shared gang member connections which is conceptually consistent with the proximity principle. According to the proximity principle, interpersonal relationships are more likely to occur in localized geographic spaces. However, gang member connections appear to be more diffuse than is captured in current location-based gang studies. This dissertation demonstrates that macro- and micro-level gang networks exist in unbounded geographic spaces where the interconnectivity of gangs transpose local issues onto the national security consciousness which challenges law and order, weakens institutions, and negatively impacts the structural integrity of the state

Data input for scientific visualization

Since the development of the modular visualization environment, the users of such gen­eral software have had to face the problems of file input Simply put, the range and complexity of different file formats has prevented the developers of visualization systems from creating an individual solution for every format. This has left a gap, where users are left to fend for themselves by either extending the system to their needs, or using a format capable of being described by one of the input tools offered by such systems. Neither of these options is particularly easy, and the use of field dependent terminology can hamper such efforts.This thesis proposes a model, architecture and methodology, for importing uncommon file formats and data into scientific visualization systems by way of interpretation. Using interpretation we are able to describe many file formats in a general manner, enabling further development of simple methods to aid users in solving their data input problems. The utility of these concepts is illustrated through the Interactive File Input Toolkit (IFIT), which allows users to solve their file input problems in a flexible manner. This tool is illustrated by a range of examples and test cases, and unlike other solutions it has the ability to discover as well as describe the content of a file. Finally, this thesis presents work towards an automatic method for determining a file’s input parameters