scholarly journals OPTIMASI JALUR EVAKUASI BAGI PEJALAN KAKI MENGGUNAKAN ALGORITMA FUZZY DIJKSTRA DI KECAMATAN TELUK SEGARA, BENGKULU

2021 ◽  
Vol 15 (3) ◽  
pp. 581-590
Author(s):  
Zulfia Memi Mayasari ◽  
Nur Afandi
Keyword(s):  
Edge Graph

Provinsi Bengkulu terletak di pesisir barat Pulau Sumatera dan tepat berada di antara pertemuan dua lempeng dunia yang sangat aktif yaitu Lempeng Benua Eurasia dan Lempeng Samudra Indo-Australia.  Hal ini  menyebabkan Provinsi Bengkulu tergolong sebagai provinsi yang sangat rawan terhadap bencana gempa bumi dan tsunami.  Penelitian ini mengkaji persoalan optimasi pencarian rute evakuasi tsunami.  Tujuan penelitian ini adalah menentukan rute evakuasi dari tiap-tiap cluster yang telah dibentuk menuju tempat berkumpul yang telah ditentukan. Tiap-tiap cluster, titik berkumpul dan ruas jalan yang menghubungkannya dibentuk menjadi suatu graph jaringan jalan dimana cluster dan titik berkumpul diwakili oleh vertex dan ruas jalan diwakili oleh edge.  Graph  jaringan jalan ini diberi bobot dimana dalam penentuan bobotnya menggunakan logika fuzzy dengan mempertimbangkan beberapa parameter yaitu panjang jalan, lebar jalan, dan jumlah penduduk yang berpengaruh pada tingkat keramaian dan tingkat kemacetan.  Penentuan rute optimalnya dilakukan dengan menggunakan algoritma Dijkstra.  Berdasarkan hasil penelitian, diperoleh rute optimal dari tiap-tiap cluster menuju tempat berkumpul yang telah ditentukan

Efficient Directed Densest Subgraph Discovery

2021 ◽  
Vol 50 (1) ◽  
pp. 33-40
Author(s):  
Chenhao Ma ◽  
Yixiang Fang ◽  
Reynold Cheng ◽  
Laks V.S. Lakshmanan ◽  
Wenjie Zhang ◽  
...  
Keyword(s):  
Approximation Algorithms ◽  
State Of The Art ◽  
Exact Algorithms ◽  
Large Datasets ◽  
Dense Subgraph ◽  
Extensive Evaluation ◽  
Wide Range ◽  
Edge Graph ◽  
Community Mining ◽  
Densest Subgraph

Given a directed graph G, the directed densest subgraph (DDS) problem refers to the finding of a subgraph from G, whose density is the highest among all the subgraphs of G. The DDS problem is fundamental to a wide range of applications, such as fraud detection, community mining, and graph compression. However, existing DDS solutions suffer from efficiency and scalability problems: on a threethousand- edge graph, it takes three days for one of the best exact algorithms to complete. In this paper, we develop an efficient and scalable DDS solution. We introduce the notion of [x, y]-core, which is a dense subgraph for G, and show that the densest subgraph can be accurately located through the [x, y]-core with theoretical guarantees. Based on the [x, y]-core, we develop both exact and approximation algorithms. We have performed an extensive evaluation of our approaches on eight real large datasets. The results show that our proposed solutions are up to six orders of magnitude faster than the state-of-the-art.

Strong isoperimetric inequality for the edge graph of a tiling of the plane

1993 ◽  
Vol 61 (6) ◽  
pp. 584-595
Author(s):  
A. Calogero
Keyword(s):  
Isoperimetric Inequality ◽  
Edge Graph ◽  
Strong Isoperimetric Inequality

Hippocampal-related memory network in multiple sclerosis: A structural connectivity analysis

2018 ◽  
Vol 25 (6) ◽  
pp. 801-810 ◽  
Author(s):  
Sara Llufriu ◽  
Maria A Rocca ◽  
Elisabetta Pagani ◽  
Gianna C Riccitelli ◽  
Elisabeth Solana ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Episodic Memory ◽  
Medial Temporal Lobe ◽  
Structural Integrity ◽  
Memory Performance ◽  
Structural Connectivity ◽  
Occipital Cortex ◽  
Network Efficiency ◽  
Edge Graph ◽  
Memory Network

Background: We used graph theoretical analysis to quantify structural connectivity of the hippocampal-related episodic memory network and its association with memory performance in multiple sclerosis (MS) patients. Methods: Brain diffusion and T1-weighted sequences were obtained from 71 MS patients and 50 healthy controls (HCs). A total of 30 gray matter regions (selected a priori) were used as seeds to perform probabilistic tractography and create connectivity matrices. Global, nodal, and edge graph theoretical properties were calculated. In patients, verbal and visuospatial memory was assessed. Results: MS patients showed decreased network strength, assortativity, transitivity, global efficiency, and increased average path length. Several nodes had decreased strength and communicability in patients, whereas insula and left temporo-occipital cortex increased communicability. Patients had widespread decreased streamline count (SC) and communicability of edges, although a few ones increased their connectivity. Worse memory performance was associated with reduced network efficiency, decreased right hippocampus strength, and reduced SC and communicability of edges related to medial temporal lobe, thalamus, insula, and occipital cortex. Conclusion: Impaired structural connectivity occurs in the hippocampal-related memory network, decreasing the efficiency of information transmission. Network connectivity measures correlate with episodic memory, supporting the relevance of structural integrity in preserving memory processes in MS.

On Directed Densest Subgraph Discovery

2021 ◽  
Vol 46 (4) ◽  
pp. 1-45
Author(s):  
Chenhao Ma ◽  
Yixiang Fang ◽  
Reynold Cheng ◽  
Laks V. S. Lakshmanan ◽  
Wenjie Zhang ◽  
...  
Keyword(s):  
State Of The Art ◽  
Directed Graphs ◽  
Exact Algorithms ◽  
Large Datasets ◽  
Dense Subgraph ◽  
Extensive Evaluation ◽  
Wide Range ◽  
Edge Graph ◽  
Community Mining ◽  
Densest Subgraph

Given a directed graph G , the directed densest subgraph (DDS) problem refers to the finding of a subgraph from G , whose density is the highest among all the subgraphs of G . The DDS problem is fundamental to a wide range of applications, such as fraud detection, community mining, and graph compression. However, existing DDS solutions suffer from efficiency and scalability problems: on a 3,000-edge graph, it takes three days for one of the best exact algorithms to complete. In this article, we develop an efficient and scalable DDS solution. We introduce the notion of [ x , y ]-core, which is a dense subgraph for G , and show that the densest subgraph can be accurately located through the [ x , y ]-core with theoretical guarantees. Based on the [ x , y ]-core, we develop exact and approximation algorithms. We further study the problems of maintaining the DDS over dynamic directed graphs and finding the weighted DDS on weighted directed graphs, and we develop efficient non-trivial algorithms to solve these two problems by extending our DDS algorithms. We have performed an extensive evaluation of our approaches on 15 real large datasets. The results show that our proposed solutions are up to six orders of magnitude faster than the state-of-the-art.

Attention-Based Node-Edge Graph Convolutional Networks for Identification of Autism Spectrum Disorder Using Multi-Modal MRI Data

2021 ◽  
pp. 374-385
Author(s):  
Yuzhong Chen ◽  
Jiadong Yan ◽  
Mingxin Jiang ◽  
Zhongbo Zhao ◽  
Weihua Zhao ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Convolutional Networks ◽  
Edge Graph

Activities: Massive Graphs, Power Laws, and the World Wide Web

2003 ◽  
Vol 96 (6) ◽  
pp. 434-439
Author(s):  
L. Charles Biehl
Keyword(s):  
Mathematical Modeling ◽  
World Wide ◽  
Transportation Networks ◽  
Power Laws ◽  
Line Segments ◽  
Massive Graphs ◽  
Straight Line ◽  
Edge Graph ◽  
The World ◽  
Set Of Points

Mathematical modeling has seen many changes over the years. These changes range from the types of situations being modeled to the types of tools used for the modeling. An extremely powerful modeling tool for many situations is the vertex-edge graph (hereafter simply called a graph). In this type of graph, a set of points, called vertices (or nodes), represent objects, people, or other ideas. The nodes can be connected with (not necessarily straight) line segments, called edges (or arcs), to show a relationship between the nodes. Graphs are used to model everything from transportation networks to groups of friends.

2-D image fusion by multiscale edge graph combination

2000 ◽  
Author(s):  
S.G. Nikolov ◽  
D.R. Bull ◽  
C.N. Canagarajah ◽  
M. Halliwell ◽  
P.N.T. Wells
Keyword(s):  
Image Fusion ◽  
Edge Graph

scholarly journals Analisis dan Perancangan Software Pengukuran Metrik Skala Kompleksitas Arsitektur Bisnis

2020 ◽  
Vol 2 (1) ◽  
pp. 59-67
Author(s):  
Rizky Noer Alif ◽  
Nisa Kholifatul Ummah ◽  
Yessy Fatmasari ◽  
Muhammad Ainul Yaqin
Keyword(s):  
Job Description ◽  
Edge Graph

Dalam menjalankan sebuah organisasi diperlukan perencanaan yang baik dari berbagai aspek termasuk dalam arsitektur bisnisnya. Penelitian kali ini menggunakan studi kasus dengan Organisasi Siswa Intra Sekolah (OSIS)  sebagai objek penelitian. Masing-masing job description komponen organisasi direprentasikan dengan model proses dari Standar Operasional Prosedur (SOP)  dan stakeholdernya dimodelkan dalam graph sehingga diperoleh node, edge model proses dan edge graph sebagai metrik skala kompleksitas arsitektur bisnis. Dari hasil perhitungan dan analisis regresi linier diperoleh skala kompleksitas berpengaruh positif terhadap organisasi yang berarti organisasi tersebut tumbuh.

scholarly journals An Exploration of the Permanent-Determinant Method

10.37236/1384 ◽  
1998 ◽  
Vol 5 (1) ◽  
Author(s):  
Greg Kuperberg
Keyword(s):  
Bipartite Graph ◽  
Perfect Matchings ◽  
New Techniques ◽  
Enumerative Combinatorics ◽  
Plane Graphs ◽  
Quotient Graph ◽  
Plane Partitions ◽  
Symmetry Classes ◽  
Edge Graph

The permanent-determinant method and its generalization, the Hafnian-Pfaffian method, are methods to enumerate perfect matchings of plane graphs that were discovered by P. W. Kasteleyn. We present several new techniques and arguments related to the permanent-determinant with consequences in enumerative combinatorics. Here are some of the results that follow from these techniques: 1. If a bipartite graph on the sphere with $4n$ vertices is invariant under the antipodal map, the number of matchings is the square of the number of matchings of the quotient graph. 2. The number of matchings of the edge graph of a graph with vertices of degree at most 3 is a power of 2. 3. The three Carlitz matrices whose determinants count $a \times b \times c$ plane partitions all have the same cokernel. 4. Two symmetry classes of plane partitions can be enumerated with almost no calculation.

scholarly journals Using Apache Spark on genome assembly for scalable overlap-graph reduction

2019 ◽  
Vol 13 (S1) ◽  
Author(s):  
Alexander J. Paul ◽  
Dylan Lawrence ◽  
Myoungkyu Song ◽  
Seung-Hwan Lim ◽  
Chongle Pan ◽  
...  
Keyword(s):  
Genome Assembly ◽  
De Novo ◽  
Time Frame ◽  
Apache Spark ◽  
Reference Sequence ◽  
Graph Reduction ◽  
De Novo Genome Assembly ◽  
String Graph ◽  
Edge Graph ◽  
Generation Sequencing

Abstract Background De novo genome assembly is a technique that builds the genome of a specimen using overlaps of genomic fragments without additional work with reference sequence. Sequence fragments (called reads) are assembled as contigs and scaffolds by the overlaps. The quality of the de novo assembly depends on the length and continuity of the assembly. To enable faster and more accurate assembly of species, existing sequencing techniques have been proposed, for example, high-throughput next-generation sequencing and long-reads-producing third-generation sequencing. However, these techniques require a large amounts of computer memory when very huge-size overlap graphs are resolved. Also, it is challenging for parallel computation. Results To address the limitations, we propose an innovative algorithmic approach, called Scalable Overlap-graph Reduction Algorithms (SORA). SORA is an algorithm package that performs string graph reduction algorithms by Apache Spark. The SORA’s implementations are designed to execute de novo genome assembly on either a single machine or a distributed computing platform. SORA efficiently compacts the number of edges on enormous graphing paths by adapting scalable features of graph processing libraries provided by Apache Spark, GraphX and GraphFrames. Conclusions We shared the algorithms and the experimental results at our project website, https://github.com/BioHPC/SORA. We evaluated SORA with the human genome samples. First, it processed a nearly one billion edge graph on a distributed cloud cluster. Second, it processed mid-to-small size graphs on a single workstation within a short time frame. Overall, SORA achieved the linear-scaling simulations for the increased computing instances.

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