Social Network Analysis Menggunakan Metode k-Path Centrality

Social Network Analysis (SNA)digunakan untuk menganalisis interaksi dalam suatu kelompok social network.Contoh penerapan SNA yaitu centrality measurement atau perhitungancentrality yang dapat digunakan untuk menentukanranking useryang berpengaruh dalam penyebaran informasi.Salah satu metode untuk perhitungan centrality adalah k-Path centrality, dimana node yang paling berpengaruh dalam penyebaran informasi adalah node yang sering dilewati jalur infromasi (path). Menentukan kombinasi kemungkinan path ini dilakukan secara random.Pada Tugas Akhir ini menerapkan metode k-Path centrality dengan pendekatan algoritma random, pertama bertujuan untuk menentukan ranking useryang berpengaruh dalam social media Twitter dan yang kedua untuk mengetahui pengaruh nilai parameter ? dalam perhitungan nilaik-Path centrality. Hasil pengujian menunjukan bahwa metode k-Path centrality dengan pendekatan algoritma random dapat digunakan untuk menentukan ranking useryang berpengaruh dalam penyebaran informasi di Twitter dan hasil yang kedua yaitu nilai ? berpengaruh terhadap waktu dan hasil perangkingan, semakin kecil nilai ? yang digunakan maka waktu yang diperlukan semakin lama namun hasil perangkingan semakin stabil. Social Network Analysis, k-Path Centrality, Algoritma Rando

Enhancing community detection using a network weighting strategy

A community within a network is a group of vertices densely connected to each other but less connected to the vertices outside. The problem of detecting communities in large networks plays a key role in a wide range of research areas, e.g. Computer Science, Biology and Sociology. Most of the existing algorithms to find communities count on the topological features of the network and often do not scale well on large, real-life instances. In this article we propose a strategy to enhance existing community detection algorithms by adding a pre-processing step in which edges are weighted according to their centrality w.r.t. the network topology. In our approach, the centrality of an edge reflects its contribute to making arbitrary graph tranversals, i.e., spreading messages over the network, as short as possible. Our strategy is able to effectively complements information about network topology and it can be used as an additional tool to enhance community detection. The computation of edge centralities is carried out by performing multiple random walks of bounded length on the network. Our method makes the computation of edge centralities feasible also on large-scale networks. It has been tested in conjunction with three state-of-the-art community detection algorithms, namely the Louvain method, COPRA and OSLOM. Experimental results show that our method raises the accuracy of existing algorithms both on synthetic and real-life datasets.Comment: 28 pages, 2 figure

Methods for network generation and spectral feature selection: especially on gene expression data

2019 Fall.Includes bibliographical references.Feature selection is an essential step in many data analysis pipelines due to its ability to remove unimportant data. We will describe how to realize a data set as a network using correlation, partial correlation, heat kernel and random edge generation methods. Then we lay out how to select features from these networks mainly leveraging the spectrum of the graph Laplacian, adjacency, and supra-adjacency matrices. We frame this work in the context of gene co-expression network analysis and proceed with a brief analysis of a small set of gene expression data for human subjects infected with the flu virus. We are able to distinguish two sets of 14-15 genes which produce two fold SSVM classification accuracies at certain times that are at least as high as classification accuracies done with more than 12,000 genes

Network depth: identifying median and contours in complex networks

Centrality descriptors are widely used to rank nodes according to specific concept(s) of importance. Despite the large number of centrality measures available nowadays, it is still poorly understood how to identify the node which can be considered as the `centre' of a complex network. In fact, this problem corresponds to finding the median of a complex network. The median is a non-parametric and robust estimator of the location parameter of a probability distribution. In this work, we present the most natural generalisation of the concept of median to the realm of complex networks, discussing its advantages for defining the centre of the system and percentiles around that centre. To this aim, we introduce a new statistical data depth and we apply it to networks embedded in a geometric space induced by different metrics. The application of our framework to empirical networks allows us to identify median nodes which are socially or biologically relevant