scholarly journals Implicit Landmark Path Indexing for Bounded Label Constrained Reachable Paths

2019 ◽  
Vol 8 (4) ◽  
pp. 10660-10669
Keyword(s):  
Directed Graphs ◽  
Interaction Networks ◽  
Unstructured Data ◽  
Edge Label ◽  
Novel Technique ◽  
Proteinprotein Interaction ◽  
Graph Reachability ◽  
Data Graph ◽  
Indexing Technique ◽  
The Given

In today’s Big Data era, a graph is an essential tool that models the semi-structured or unstructured data. Graph reachability with vertex or edge constraints is one of the basic queries to extract useful information from the graph data. From the graph reachability with constraints, we obtained the information about the existence of a path between the given two vertices satisfying the vertex or edge constraints. The problem of Label Constraint Reachability (LCR) found the existence of a path between the two given vertices such that the edge-labels along the path are the subset of the given edge-label constraint. We extended the LCR queries by considering weighted directed graphs and proposed a novel technique of finding paths for LCR queries bounded by path weight. We termed these paths as bounded label constrained reachable paths (BLCRP). We extended the landmark path indexing technique [1] by incorporating the implicit paths which satisfy the user constraints but need not satisfy the minimality of edge label sets. We solved the BLCRP by using the extended landmark path indexing and BFS based query processing. We addressed the following challenges through our proposed technique of implicit landmark path indexing in the problem of BLCRP that included (1) the need to handle exponential number of edge label combinations with an additional total path weight constraint, and (2) the need to discover a technique that finds exact reachable paths between the given vertices. This problem could be applied to real network scenarios like road networks, social networks, and proteinprotein interaction networks. Our experiments and statistical analysis revealed the accuracy and efficiency of the proposed approach tested on synthetic and real datasets.

scholarly journals Identifying Social Media Influencers using Graph Analytics

2020 ◽  
Author(s):  
Pankti Joshi ◽  
Sabah Mohammed
Keyword(s):  
Social Media ◽  
Community Structure ◽  
Social Network ◽  
Network Analysis ◽  
Unstructured Data ◽  
Graph Analytics ◽  
Content Sharing ◽  
Twitter Data ◽  
Data Graph ◽  
Flow Of Information

<div>Social network analysis has been an essential topic</div><div>with broad content sharing from social media. Defining the</div><div>directed links in social media determine the flow of information and indicates the user’s influence. Due to the enormous data and unstructured nature of sharing information, there are several challenges caused while handling data. Graph Analytics proves to be an essential tool for addressing problems such as building networks from unstructured data, inferring information from the system, and analyzing the community structure of a network. The proposed approach aims to determine the influencers on Twitter data, based on the follower’s count as well as the retweet count. Several graph-based algorithms are implemented on the data collected to find the influencer as well as communities in the network.</div>

scholarly journals Identifying Social Media Influencers using Graph Analytics

2020 ◽  
Author(s):  
Pankti Joshi ◽  
Sabah Mohammed
Keyword(s):  
Social Media ◽  
Community Structure ◽  
Social Network ◽  
Network Analysis ◽  
Unstructured Data ◽  
Graph Analytics ◽  
Content Sharing ◽  
Twitter Data ◽  
Data Graph ◽  
Flow Of Information

<div>Social network analysis has been an essential topic</div><div>with broad content sharing from social media. Defining the</div><div>directed links in social media determine the flow of information and indicates the user’s influence. Due to the enormous data and unstructured nature of sharing information, there are several challenges caused while handling data. Graph Analytics proves to be an essential tool for addressing problems such as building networks from unstructured data, inferring information from the system, and analyzing the community structure of a network. The proposed approach aims to determine the influencers on Twitter data, based on the follower’s count as well as the retweet count. Several graph-based algorithms are implemented on the data collected to find the influencer as well as communities in the network.</div>

scholarly journals Learning with Adaptive Neighbors for Image Clustering

2018 ◽  
Author(s):  
Yang Liu ◽  
Quanxue Gao ◽  
Zhaohua Yang ◽  
Shujian Wang
Keyword(s):  
State Of The Art ◽  
Clustering Algorithms ◽  
Original Data ◽  
Image Clustering ◽  
Complex Structures ◽  
Clustering Methods ◽  
Proposed Model ◽  
Data Graph ◽  
The Given ◽  
Optimal Graph

Due to the importance and efficiency of learning complex structures hidden in data, graph-based methods have been widely studied and get successful in unsupervised learning. Generally, most existing graph-based clustering methods require post-processing on the original data graph to extract the clustering indicators. However, there are two drawbacks with these methods: (1) the cluster structures are not explicit in the clustering results; (2) the final clustering performance is sensitive to the construction of the original data graph. To solve these problems, in this paper, a novel learning model is proposed to learn a graph based on the given data graph such that the new obtained optimal graph is more suitable for the clustering task. We also propose an efficient algorithm to solve the model. Extensive experimental results illustrate that the proposed model outperforms other state-of-the-art clustering algorithms.

scholarly journals Data Storing Proposal from Heterogeneous Systems into a Specialized Repository

2016 ◽  
Vol 24 (39) ◽  
pp. 123-128
Author(s):  
Andrea Václavová ◽  
Pavol Tanuška ◽  
Ján Jánošík
Keyword(s):  
Detailed Analysis ◽  
Heterogeneous Systems ◽  
Unstructured Data ◽  
Data Repositories ◽  
Transfer Data ◽  
The Third ◽  
The Given

Abstract The aim of this paper is to analyze and to propose an appropriate system for processing and simultaneously storing a vast volume of structured and unstructured data. The paper consists of three parts. The first part addresses the issue of structured and unstructured data. The second part provides the detailed analysis of data repositories and subsequent evaluation indicating which system would be for the given type and volume of data optimal. The third part focuses on the use of gathered information to transfer data to the proposed repository.

scholarly journals A Novel Network Approach to Capture Cognition and Affect: COVID-19 Experiences in Canada and Germany

2021 ◽  
Vol 12 ◽  
Author(s):  
Jordan Mansell ◽  
Lisa Reuter ◽  
Carter Rhea ◽  
Andrea Kiesel
Keyword(s):  
Quantitative Methods ◽  
Directed Graphs ◽  
Software Tool ◽  
Structured Interviews ◽  
Qualitative And Quantitative Methods ◽  
Significant Relationships ◽  
Novel Method ◽  
The Given ◽  
The Relationship ◽  
Viral Outbreak

We tested a novel method for studying human experience (thoughts and affect). We utilized Cognitive-Affective Maps (CAMs)–an approach to visually represent thoughts and their affective connotations as networks of concepts that individuals associate with a given event. Using an innovative software tool, we recruited a comparative sample of (n = 93) Canadians and (n = 100) Germans to draw a CAM of their experience (events, thoughts, feelings) with the Covid-19 pandemic. We treated these CAM networks as a series of directed graphs and examined the extent to which their structural properties (latent and emotional) are predictive for the perceived coronavirus threat (PCT). Across multiple models, we found consistent and significant relationships between these network variables and the PCT in both the Canadian and German sample. Our results provide unique insights into individuals' thinking and perceptions of the viral outbreak. Our results also demonstrate that a network analysis of CAMs' properties is a promising method to study the relationship between human thought and affective connotation. We suggest that CAMs can bridge several gaps between qualitative and quantitative methods. Unlike when using quantitative tools (e.g., questionnaires), participants' answers are not restricted by response items as participants are free to incorporate any thoughts and feelings on the given topic. Furthermore, as compared to traditional qualitative measures, such as structured interviews, the CAM technique may better enable researchers to objectively assess and integrate the substance of a shared experience for large samples of participants.

A Novel Technique for the Stability of Multiple Frequency Resonators

1999 ◽  
Author(s):  
Nejat Olgac
Keyword(s):  
Stability Analysis ◽  
Time Delays ◽  
Dimensional Space ◽  
Multiple Delays ◽  
Stability Assessment ◽  
Multiple Frequency ◽  
Novel Technique ◽  
Controlled Systems ◽  
The Stability ◽  
The Given

Abstract A novel stability analysis is presented for feedback controlled systems with multiple unrelated time delays. The concern arises in an effort creating multi-frequency delayed resonators. Despite the broad treatment of single time delay cases there is no mechanism to analyze the stability of systems involving multiple delays. The proposed method is an advancement over the D-subdivision methodology. It reduces the stability based subdivisions and search to a single dimensional space instead of multiple dimensional platform. The method is very simple to implement and it results in the stability assessment of the given system as the number of unstable root pairs present.

Graph Pattern Matching: Capturing Bisimilar Subgraph

2020 ◽  
pp. 2150011
Author(s):  
Rui Qiao ◽  
Ke Feng ◽  
Heng He ◽  
Xiaolei Zhong
Keyword(s):  
Pattern Matching ◽  
Graph Matching ◽  
Directed Graphs ◽  
Graph Databases ◽  
Graph Pattern Matching ◽  
Query Graph ◽  
Graph Pattern ◽  
Finite Directed Graphs ◽  
Data Graph ◽  
Text Language

Graph pattern matching that aims to seek out answer graphs in a data graph matching a provided graph, plays a fundamental role as a part of graph search for graph databases. “Matching” indicates that the two graphs are correlated, such as bisimulation, isomorphism, simulation, etc. The strictness of bisimulation is between simulation and isomorphism. Seldom work has been done to search for bisimulation subgraphs. This research focuses on the problem. The symbol [Formula: see text] is introduced to fundamental modal logic language, thereby yielding [Formula: see text] language; the symbols [Formula: see text] is added for forming [Formula: see text] formulas. Then conclusions about graph bisimulations are shown. Subsequently, a theorem with detailed proof is presented, stating that [Formula: see text] formulas characterize finite directed graphs modulo bisimulation. According to the conclusions and theorem, algorithms for finding subgraphs are proposed. After dividing the query graph, the match graphs undergo the characterization using [Formula: see text] formulas. In the data graphs, by model checking the formulas, the answer graphs exhibiting bisimilarity to the match graphs are able to be captured.

A Novel Technique in the Solution of Axisymmetric Large Deflection Analysis of a Circular Plate

2001 ◽  
Vol 68 (5) ◽  
pp. 814-816 ◽  
Author(s):  
L. S. Ramachandra ◽  
D. Roy
Keyword(s):  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Large Deflection ◽  
Nonlinear Ordinary Differential Equations ◽  
Algebraic Equations ◽  
Circular Plates ◽  
Novel Technique ◽  
Nonlinear Algebraic Equations ◽  
Deflection Analysis ◽  
The Given

In the present paper a new linearization technique referred to as the locally transversal linearization (LTL) is used for large deflection analyses of axisymmetric circular plates. The LTL procedure, where solution manifolds of linearized equations are made to intersect transversally those of the nonlinear ordinary differential equations, reduces the given set of nonlinear ordinary differential equations to a set of nonlinear algebraic equations in terms of a descretized set of unknown response vectors.

Discovering Protein Complexes in Protein Interaction Networks

2009 ◽  
pp. 144-168 ◽  
Author(s):  
Clara Pizzuti ◽  
Simona Ester Rombo
Keyword(s):  
Protein Complexes ◽  
Research Problem ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Putative Protein ◽  
Cell Life ◽  
Starting Point ◽  
Computer Scientists ◽  
Proteinprotein Interaction ◽  
Graph Based Clustering

In this chapter a survey on the main graph-based clustering techniques proposed in the literature to mine proteinprotein interaction networks (PINs) is presented. The detection of putative protein complexes is an important research problem in systems biology. In fact it may help in understanding the mechanisms regulating cell life, in deriving conservations across species, in predicting the biological functions of uncharacterized proteins, and, more importantly, for therapeutic purposes. Different kind of approaches are described and classified. Furthermore, some validation techniques commonly exploited in this context are illustrated. The goal of the chapter is to provide a useful guide and reference for both computer scientists and biologists. Computer scientists may have a complete vision of what has already been made and which are the new challenges about PINs clustering, taking them as a starting point for further researches and new proposals; on the other hand, biologists may find in the chapter the necessary material to select the most appropriate methods to apply for their specific purposes.

Business Intelligence

2021 ◽  
pp. 191-206
Author(s):  
Jakia Sultana ◽  
Ahmed Jimoh
Keyword(s):  
Artificial Intelligence ◽  
Business Intelligence ◽  
Organizational Context ◽  
Unstructured Data ◽  
Future Outlook ◽  
The Future ◽  
The Given

This chapter discussed business intelligence (BI), highlighting its general strengths, weaknesses, and opportunities in the organizational context and in the context of unstructured data. Initially, a brief background on BI was discussed, followed by the discussion on benefit and challenges in different context. Recommendations provided for the challenges were discussed. Later, the chapter further looked at business intelligence and artificial intelligence followed by the future outlook of business intelligence. The contents of this chapter will help theoretically to understand the business intelligence, its background, benefits and challenges, and how to deal with the challenges by the given recommendations. Practically, this chapter will give insight to organizations about challenges to think about earlier stage based on the discussion on challenges in the organizational context.

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