Dynamic Multilevel Graph Visualization

We adapt multilevel, force-directed graph layout techniques to visualizing dynamic graphs in which vertices and edges are added and removed in an online fashion (i.e., unpredictably). We maintain multiple levels of coarseness using a dynamic, randomized coarsening algorithm. To ensure the vertices follow smooth trajectories, we employ dynamics simulation techniques, treating the vertices as point particles. We simulate fine and coarse levels of the graph simultaneously, coupling the dynamics of adjacent levels. Projection from coarser to finer levels is adaptive, with the projection determined by an affine transformation that evolves alongside the graph layouts. The result is a dynamic graph visualizer that quickly and smoothly adapts to changes in a graph.Comment: 21 page

Structure induction by lossless graph compression

This work is motivated by the necessity to automate the discovery of structure in vast and evergrowing collection of relational data commonly represented as graphs, for example genomic networks. A novel algorithm, dubbed Graphitour, for structure induction by lossless graph compression is presented and illustrated by a clear and broadly known case of nested structure in a DNA molecule. This work extends to graphs some well established approaches to grammatical inference previously applied only to strings. The bottom-up graph compression problem is related to the maximum cardinality (non-bipartite) maximum cardinality matching problem. The algorithm accepts a variety of graph types including directed graphs and graphs with labeled nodes and arcs. The resulting structure could be used for representation and classification of graphs.Comment: 10 pages, 7 figures, 2 tables published in Proceedings of the Data Compression Conference, 200

Interactive Search and Exploration in Online Discussion Forums Using Multimodal Embeddings

In this paper we present a novel interactive multimodal learning system, which facilitates search and exploration in large networks of social multimedia users. It allows the analyst to identify and select users of interest, and to find similar users in an interactive learning setting. Our approach is based on novel multimodal representations of users, words and concepts, which we simultaneously learn by deploying a general-purpose neural embedding model. We show these representations to be useful not only for categorizing users, but also for automatically generating user and community profiles. Inspired by traditional summarization approaches, we create the profiles by selecting diverse and representative content from all available modalities, i.e. the text, image and user modality. The usefulness of the approach is evaluated using artificial actors, which simulate user behavior in a relevance feedback scenario. Multiple experiments were conducted in order to evaluate the quality of our multimodal representations, to compare different embedding strategies, and to determine the importance of different modalities. We demonstrate the capabilities of the proposed approach on two different multimedia collections originating from the violent online extremism forum Stormfront and the microblogging platform Twitter, which are particularly interesting due to the high semantic level of the discussions they feature

VISUALISASI DAN DOKUMENTASI METODE LWBA SECARA OTOMATIS DAN KONSISTEN UNTUK REQUIREMENT ENGINEERING

Visualisasi dalam requirement engineering dibutuhkan agar penyebab permasalahan menjadi lebih terjajaki (traceable). Di samping visualisasi, dibutuhkan juga deskripsi dari visualisasi. Pada fase requirement engineering, kadangkala terjadi inkonsistensi penyampaian informasi. Oleh karena itu, dibutuhkan visualisasi dan dokumentasi informasi secara otomatis dan konsisten untuk menelusuri penyebab ketidakpuasan. Untuk itu diperlukan suatu perangkat bantu agar memperoleh visualisasi dan dokumentasi secara otomatis dan konsisten. Dengan dikembangkannya perangkat bantu untuk visualisasi dan pendeskripsian informasi LWBA dengan menggunakan metode BBSDM diharapkan penyajian informasi menjadi lebih baik secara otomatis

Automated server-side model for recognition of security vulnerabilities in scripting languages

With the increase of global accessibility of web applications, maintaining a reasonable security level for both user data and server resources has become an extremely challenging issue. Therefore, static code analysis systems can help web developers to reduce time and cost. In this paper, a new static analysis model is proposed. This model is designed to discover the security problems in scripting languages. The proposed model is implemented in a prototype SCAT, which is a static code analysis Tool. SCAT applies the phases of the proposed model to catch security vulnerabilities in PHP 5.3. Empirical results attest that the proposed prototype is feasible and is able to contribute to the security of real-world web applications. SCAT managed to detect 94% of security vulnerabilities found in the testing benchmarks; this clearly indicates that the proposed model is able to provide an effective solution to complicated web systems by offering benefits of securing private data for users and maintaining web application stability for web applications providers

Visualizing Multivariate Hierarchic Data Using Enhanced Radial Space-Filling Layout

Currently, visualization tools for large ontologies (e.g., pathway and gene ontologies) result in a very flat wide tree that is difficult to fit on a single display. This paper develops the concept of using an enhanced radial space-filling (ERSF) layout to show biological ontologies efficiently. The ERSF technique represents ontology terms as circular regions in 3D. Orbital connections in a third dimension correspond to non-tree edges in the ontology that exist when an ontology term belongs to multiple categories. Biologists can use the ERSF layout to identify highly activated pathway or gene ontology categories by mapping experimental statistics such as coefficient of variation and overrepresentation values onto the visualization. This paper illustrates the use of the ERSF layout to explore pathway and gene ontologies using a gene expression dataset from E. coli

BiGGEsTS: integrated environment for biclustering analysis of time series gene expression data

<p>Abstract</p> <p>Background</p> <p>The ability to monitor changes in expression patterns over time, and to observe the emergence of coherent temporal responses using expression time series, is critical to advance our understanding of complex biological processes. Biclustering has been recognized as an effective method for discovering local temporal expression patterns and unraveling potential regulatory mechanisms. The general biclustering problem is NP-hard. In the case of time series this problem is tractable, and efficient algorithms can be used. However, there is still a need for specialized applications able to take advantage of the temporal properties inherent to expression time series, both from a computational and a biological perspective.</p> <p>Findings</p> <p>BiGGEsTS makes available state-of-the-art biclustering algorithms for analyzing expression time series. Gene Ontology (GO) annotations are used to assess the biological relevance of the biclusters. Methods for preprocessing expression time series and post-processing results are also included. The analysis is additionally supported by a visualization module capable of displaying informative representations of the data, including heatmaps, dendrograms, expression charts and graphs of enriched GO terms.</p> <p>Conclusion</p> <p>BiGGEsTS is a free open source graphical software tool for revealing local coexpression of genes in specific intervals of time, while integrating meaningful information on gene annotations. It is freely available at: <url>http://kdbio.inesc-id.pt/software/biggests</url>. We present a case study on the discovery of transcriptional regulatory modules in the response of <it>Saccharomyces cerevisiae </it>to heat stress.</p

PEMANFAATAN NETWORKX UNTUK MENGEKSPLORASI DAN MENGANALISA JARINGAN BESERTA SIFAT/KARAKTERISTIKNYA

Aplikasi analisis jaringan telah banyak ditemukan di berbagai penelitian ilmiah jaringan transport, jaringan sosial, analisis trafik internet, jaringan penyebaran virus baik di bidang kesehatan maupun teknologi informasi, serta jaringan hubungan protein dan gen di bidang kesehatan dan biologi. Kemajuan teknologi yang sangat pesat telah memotivasi para peneliti di bidang kombinatorial optimisasi untuk mempelajari jaringan dengan skala besar. NetworkX merupakan suatu paket perangkat lunak berbahasa Python yang dapat digunakan untuk membentuk, memanipulasi dan mempelajari struktur, dinamika serta fungsi dari jaringan kecil hingga jaringan yang rumit/besar. Makalah ini membahas tentang berbagai bentuk dari jaringan (atau graf) yang dapat dieksplorasi dan dianalisa dengan NetworkX, serta algoritma yang dapat diimplementasikan untuk menghitung beberapa sifat atau karakteristik penting dari graf, khususnya pada graf-graf yang digunakan dalam berbagai penelitian optimisasi (benchmark instances). Kata kunci: NetworkX, jaringan, graf, instance features