Generating large scale undirected graph for solving flow network problems

2010 ◽  
Author(s):  
S. G. Chen
Keyword(s):  
Large Scale ◽  
Undirected Graph ◽  
Flow Network ◽  
Network Problems

scholarly journals Efficient Algorithms for Identifying Loop Formation and Computing θ Value for Solving Minimum Cost Flow Network Problems

2021 ◽  
Vol 20 ◽  
pp. 107-117
Author(s):  
TIMOTHY MICHAEL CHÁVEZ ◽  
DUC THAI NGUYEN
Keyword(s):  
Real Life ◽  
Minimum Cost ◽  
Simplex Algorithm ◽  
Efficient Algorithms ◽  
Computer Implementation ◽  
Loop Formation ◽  
Minimum Cost Flow ◽  
Flow Network ◽  
Network Problems ◽  
Cost Flow

While the minimum cost flow (MCF) problems have been well documented in many publications, due to its broad applications, little or no effort have been devoted to explaining the algorithms for identifying loop formation and computing the θ value needed to solve MCF network problems. This paper proposes efficient algorithms, and MATLAB computer implementation, for solving MCF problems. Several academic and real-life network problems have been solved to validate the proposed algorithms; the numerical results obtained by the developed MCF code have been compared and matched with the built-in MATLAB function Linprog() (Simplex algorithm) for further validation.

scholarly journals Finding Dense Components in Large-Scale Network Using Randomized Binary Search Tree

2020 ◽  
Vol 30.8 (147) ◽  
pp. 65-69
Author(s):  
Anh Phuc Trinh ◽  
◽  
Dang Hai Pham ◽  
Thi Thuy Dung Phan ◽  
Keyword(s):  
Large Scale ◽  
Complexity Analysis ◽  
Undirected Graph ◽  
Search Tree ◽  
Binary Search ◽  
Binary Search Tree ◽  
Large Scale Network ◽  
The Social ◽  
Vertex Set ◽  
Scale Network

Given a simple undirected graph G=(V, E), the density of a subgraph on vertex set S is defined as a ratio between the number of edges |E(S)| and the number of vertices |S|, where E(S) is the set of edges induced by vertices in S. Finding the maximum density subgraph has become an intense study in recent years, especially in the social network era. Being based on a greedy algorithm that connects with a suitable graph data structure, we have reduced its time complexity by using a randomized binary search tree, also called treap. We make the complexity analysis in both time and memory requirements, including computational experiments in large scale real networks.

scholarly journals A Case Study of Pyramid Scheme Finance Flow Network Based on Social Network Analysis

2019 ◽  
Vol 11 (16) ◽  
pp. 4370
Author(s):  
Feng ◽  
Sun ◽  
Gong
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Large Scale ◽  
Graph Model ◽  
Flow Network ◽  
Random Graph Model ◽  
Exponential Random Graph ◽  
Pyramid Structure ◽  
Pyramid Scheme

(1) Background: The pyramid scheme has caused a large-scale plunder of finances due to the unsustainability of its operating model, which seriously jeopardizes economic development and seriously affects social stability. In various types of networks, the finance flow network plays an extremely important role in the pyramid scheme organization. Through the study of the finance network, the operational nature of pyramid scheme organizations can be effectively explored, and the understanding of pyramid scheme organizations can be deepened to provide a basis for dealing with them. (2) Methods: This paper uses the motifs analysis and exponential random graph model in social network analysis to study the micro-structure and the network construction process of the “5.03” pyramid scheme finance flow network in Hunan, China. (3) Results: The finance flow network is sparse, the microstructure shows a typical pyramid structure; finance flows within the community and eventually flows to the most critical personnel, there is no finance relationship between different communities, and there are few finance relationships between pyramid salesmen of the same level. The inductees are in a key position in the network, which may explain why they are transferred to prosecution.

scholarly journals Study on natural ventilation of large-scale indoor space Flow network analysis of buoyancy- and wind-induced ventilation of whole-sale market building

1990 ◽  
Vol 1990 (45) ◽  
pp. 45-55
Author(s):  
Shuzo MURAKAMI ◽  
Shinsuke KATO ◽  
Shin-ichi AKABAYASHI ◽  
Takeo TAKAHASHI ◽  
Kunio MIZUTANI ◽  
...  
Keyword(s):  
Network Analysis ◽  
Large Scale ◽  
Natural Ventilation ◽  
Flow Network ◽  
Indoor Space

Real-Time Dynamic Origin-Destination Matrix Adjustment with Simulated and Actual Link Flows in Urban Networks

2003 ◽  
Vol 1857 (1) ◽  
pp. 117-127 ◽  
Author(s):  
Theodore Tsekeris ◽  
Antony Stathopoulos
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Dynamic Traffic Assignment ◽  
Computational Time ◽  
Urban Networks ◽  
Time Dynamic ◽  
Assignment Model ◽  
Network Problems ◽  
Diverse Application ◽  
Scale Network

The efficiency and robustness of different real-time dynamic origin–destination (O-D) matrix adjustment algorithms were investigated when implemented in large-scale transportation networks. The proposed algorithms produce time-dependent O-D trip matrices based on the maximum-entropy trip departure times with simulated and actual observed link flows. Implementation of the algorithms, which are coupled with a quasi-dynamic traffic assignment model, indicated their convergent behavior and their potential for handling realistic urban-scale network problems in terms of both accuracy and computational time. The main factors influencing the numerical performance of each algorithm were identified and analyzed. Their relative efficiency was found to be particularly dependent on the level at which the assigned flows approximate the observed link flows. These results may provide insights into the suitability of each algorithm for diverse application domains, including freeways, small networks, and large-scale urban networks, where a different quality of O-D information is usually available.

scholarly journals Decision Diagram Based Symbolic Algorithm for Evaluating the Reliability of a Multistate Flow Network

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Rongsheng Dong ◽  
Yangyang Zhu ◽  
Zhoubo Xu ◽  
Fengying Li
Keyword(s):  
Large Scale ◽  
Complexity Analysis ◽  
Decision Diagrams ◽  
Binary Decision ◽  
Flow Network ◽  
Memory Space ◽  
Efficient Data ◽  
Np Hard Problem ◽  
Efficient Data Structures ◽  
Symbolic Algorithms

Evaluating the reliability of Multistate Flow Network (MFN) is an NP-hard problem. Ordered binary decision diagram (OBDD) or variants thereof, such as multivalued decision diagram (MDD), are compact and efficient data structures suitable for dealing with large-scale problems. Two symbolic algorithms for evaluating the reliability of MFN, MFN_OBDD and MFN_MDD, are proposed in this paper. In the algorithms, several operating functions are defined to prune the generated decision diagrams. Thereby the state space of capacity combinations is further compressed and the operational complexity of the decision diagrams is further reduced. Meanwhile, the related theoretical proofs and complexity analysis are carried out. Experimental results show the following: (1) compared to the existing decomposition algorithm, the proposed algorithms take less memory space and fewer loops. (2) The number of nodes and the number of variables of MDD generated in MFN_MDD algorithm are much smaller than those of OBDD built in the MFN_OBDD algorithm. (3) In two cases with the same number of arcs, the proposed algorithms are more suitable for calculating the reliability of sparse networks.

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