The document proposes a new layout called Group-In-a-Box (GIB) for visualizing clustered graphs that enables multi-faceted analysis of networks. GIB uses the treemap technique to display each graph cluster or category group within its own box, sized according to the number of vertices. This allows analysis of both community structure detected from network clustering algorithms as well as categories of individuals. The document demonstrates GIB on real social networks and synthetic networks, showing it can reveal intra-group network structures and the attributes of members in different groups.
Subgraph Frequencies: Mapping the Empirical and Extremal Geography of Large G...Gabriela Agustini
A growing set of on-line applications are generating data that can be viewed as very large collections of small, dense social graphs — these range from sets of social groups, events, or collabora- tion projects to the vast collection of graph neighborhoods in large social networks. A natural question is how to usefully define a domain-independent ‘coordinate system’ for such a collection of graphs, so that the set of possible structures can be compactly rep- resented and understood within a common space. In this work, we draw on the theory of graph homomorphisms to formulate and an- alyze such a representation, based on computing the frequencies of small induced subgraphs within each graph.
The community detection in complex networks has attracted a growing interest and is the subject of several
researches that have been proposed to understand the network structure and analyze the network
properties. In this paper, we give a thorough overview of different community discovery strategies, we
propose taxonomy of these methods, and we specify the differences between the suggested classes which
helping designers to compare and choose the most suitable strategy for the various types of network
encountered in the real world.
A Proposed Algorithm to Detect the Largest Community Based On Depth LevelEswar Publications
The incredible rising of online networks show that these networks are complex and involving massive data.Giving a very strong interest to set of techniques developed for mining these networks. The clique problem is a well known NP-Hard problem in graph mining. One of the fundamental applications for it is the community detection. It helps to understand and model the network structure which has been a fundamental problem in several fields. In literature, the exponentially increasing computation time of this problem make the quality of these solutions is limited and infeasible for massive graphs. Furthermore, most of the proposed approaches are able to detect only disjoint communities. In this paper, we present a new clique based approach for fast and efficient overlapping
community detection. The work overcomes the short falls of clique percolation method (CPM), one of most popular and commonly used methods in this area. The shortfalls occur due to brute force algorithm for enumerating maximal cliques and also the missing out many vertices thatleads to poor node coverage. The proposed work overcome these shortfalls producing NMC method for enumerating maximal cliques then detects overlapping communities using three different community scales based on three different depth levels to assure high nodes coverage and detects the largest communities. The clustering coefficient and cluster density are used to measure the quality. The work also provide experimental results on benchmark real world network to
demonstrate the efficiency and compare the new proposed algorithm with CPM method, The proposed algorithm is able to quickly discover the maximal cliques and detects overlapping community with interesting remarks and findings.
Subgraph Frequencies: Mapping the Empirical and Extremal Geography of Large G...Gabriela Agustini
A growing set of on-line applications are generating data that can be viewed as very large collections of small, dense social graphs — these range from sets of social groups, events, or collabora- tion projects to the vast collection of graph neighborhoods in large social networks. A natural question is how to usefully define a domain-independent ‘coordinate system’ for such a collection of graphs, so that the set of possible structures can be compactly rep- resented and understood within a common space. In this work, we draw on the theory of graph homomorphisms to formulate and an- alyze such a representation, based on computing the frequencies of small induced subgraphs within each graph.
The community detection in complex networks has attracted a growing interest and is the subject of several
researches that have been proposed to understand the network structure and analyze the network
properties. In this paper, we give a thorough overview of different community discovery strategies, we
propose taxonomy of these methods, and we specify the differences between the suggested classes which
helping designers to compare and choose the most suitable strategy for the various types of network
encountered in the real world.
A Proposed Algorithm to Detect the Largest Community Based On Depth LevelEswar Publications
The incredible rising of online networks show that these networks are complex and involving massive data.Giving a very strong interest to set of techniques developed for mining these networks. The clique problem is a well known NP-Hard problem in graph mining. One of the fundamental applications for it is the community detection. It helps to understand and model the network structure which has been a fundamental problem in several fields. In literature, the exponentially increasing computation time of this problem make the quality of these solutions is limited and infeasible for massive graphs. Furthermore, most of the proposed approaches are able to detect only disjoint communities. In this paper, we present a new clique based approach for fast and efficient overlapping
community detection. The work overcomes the short falls of clique percolation method (CPM), one of most popular and commonly used methods in this area. The shortfalls occur due to brute force algorithm for enumerating maximal cliques and also the missing out many vertices thatleads to poor node coverage. The proposed work overcome these shortfalls producing NMC method for enumerating maximal cliques then detects overlapping communities using three different community scales based on three different depth levels to assure high nodes coverage and detects the largest communities. The clustering coefficient and cluster density are used to measure the quality. The work also provide experimental results on benchmark real world network to
demonstrate the efficiency and compare the new proposed algorithm with CPM method, The proposed algorithm is able to quickly discover the maximal cliques and detects overlapping community with interesting remarks and findings.
Scalable Local Community Detection with Mapreduce for Large NetworksIJDKP
Community detection from complex information networks draws much attention from both academia and
industry since it has many real-world applications. However, scalability of community detection algorithms
over very large networks has been a major challenge. Real-world graph structures are often complicated
accompanied with extremely large sizes. In this paper, we propose a MapReduce version called 3MA that
parallelizes a local community identification method which uses the $M$ metric. Then we adopt an
iterative expansion approach to find all the communities in the graph. Empirical results show that for large
networks in the order of millions of nodes, the parallel version of the algorithm outperforms the traditional
sequential approach to detect communities using the M-measure. The result shows that for local community
detection, when the data is too big for the original M metric-based sequential iterative expension approach
to handle, our MapReduce version 3MA can finish in a reasonable time.
Community detection of political blogs network based on structure-attribute g...IJECEIAES
Complex networks provide means to represent different kinds of networks with multiple features. Most biological, sensor and social networks can be represented as a graph depending on the pattern of connections among their elements. The goal of the graph clustering is to divide a large graph into many clusters based on various similarity criteria’s. Political blogs as standard social dataset network, in which it can be considered as blog-blog connection, where each node has political learning beside other attributes. The main objective of work is to introduce a graph clustering method in social network analysis. The proposed Structure-Attribute Similarity (SAS-Cluster) able to detect structures of community, based on nodes similarities. The method combines topological structure with multiple characteristics of nodes, to earn the ultimate similarity. The proposed method is evaluated using well-known evaluation measures, Density, and Entropy. Finally, the presented method was compared with the state-of-art comparative method, and the results show that the proposed method is superior to the comparative method according to the evaluations measures.
1. Basics of Social Networks
2. Real-world problem
3. How to construct graph from real-world problem?
4. What graph theory problem getting from real-world problem?
5. Graph type of Social Networks
6. Special properties in social graph
7. How to find communities and groups in social networks? (Algorithms)
8. How to interpret graph solution back to real-world problem?
Evolving social data mining and affective analysis Athena Vakali
Evolving social data mining and affective analysis methodologies, framework and applications - Web 2.0 facts and social data
Social associations and all kinds of graphs
Evolving social data mining
Emotion-aware social data analysis
Frameworks and Applications
Clustering in Aggregated User Profiles across Multiple Social Networks IJECEIAES
A social network is indeed an abstraction of related groups interacting amongst themselves to develop relationships. However, toanalyze any relationships and psychology behind it, clustering plays a vital role. Clustering enhances the predictability and discoveryof like mindedness amongst users. This article’s goal exploits the technique of Ensemble Kmeans clusters to extract the entities and their corresponding interestsas per the skills and location by aggregating user profiles across the multiple online social networks. The proposed ensemble clustering utilizes known K-means algorithm to improve results for the aggregated user profiles across multiple social networks. The approach produces an ensemble similarity measure and provides 70% better results than taking a fixed value of K or guessing a value of K while not altering the clustering method. This paper states that good ensembles clusters can be spawned to envisage the discoverability of a user for a particular interest.
Fuzzy AndANN Based Mining Approach Testing For Social Network AnalysisIJERA Editor
Fast and Appropriate Social Network Analysis (SNA) tools ,techniques, are required to collect and classify
opinion scores on social networksites , as a grouping on wrong opinion may create problems for a society or
country . Social Network Analysis (SNA) is popular means for researcher as the number of users and groups
increasing day by day on that social sites , and a large group may influence other.In this paper, we
recommendhybrid model of opinion recommendation systems, for single user and for collective community
respectively, formed on social liking and influence network theory. By collecting thedata of user social networks
and preferenceslike, we designed aimproved hybrid prototype to imitate the social influence by like and sharing
the information among groups.The significance of this paper to analyze the suitability of ANN and Fuzzy sets
method in a hybrid manner for social web sites classifications, First, we intend to use Artificial Neural
Network(ANN)techniques in social media data classification by using some contemporary methods different
than the conventional methods of statistics and data analysis, in next we want to propagate the fuzzy approach
as a way to overcome the uncertainity that is always present in social media analysis . We give a brief overview
of the main ideas and recent results of social networks analysis , and we point to relationships between the two
social network analysis and classification approaches .This researchsuggests a hybrid classification model build
on fuzzy and artificial neural network (HFANN). Information Gain and three popular social sites are used to
collect information depicting features that are then used to train and test the proposed methods . This neoteric
approach combines the advantages of ANN and Fuzzy sets in classification accuracy with utilizing social data
and knowledge base available in the hate lexicons.
Anonymization of centralized and distributed social networks by sequential cl...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Community detection from research papers (AAN dataset) using the algorithms:
K-Means
Louvain
Newman-Girvan
github link to code: https://goo.gl/CXej44
github link to project web page: http://goo.gl/7OOkhI
youtube link to video:https://goo.gl/SCpamf
dropbox link to ppt report video: https://goo.gl/cgACzU
The problem of matchmaking in electronic social networks is formulated as an optimization problem.
In particular, a function measuring the matching degree of fields of interest of a search profile with
those of an advertising profile is proposed.
SCALABLE LOCAL COMMUNITY DETECTION WITH MAPREDUCE FOR LARGE NETWORKSIJDKP
Community detection from complex information networks draws much attention from both academia and
industry since it has many real-world applications. However, scalability of community detection algorithms
over very large networks has been a major challenge. Real-world graph structures are often complicated
accompanied with extremely large sizes. In this paper, we propose a MapReduce version called 3MA that
parallelizes a local community identification method which uses the $M$ metric. Then we adopt an
iterative expansion approach to find all the communities in the graph. Empirical results show that for large
networks in the order of millions of nodes, the parallel version of the algorithm outperforms the traditional
sequential approach to detect communities using the M-measure. The result shows that for local community
detection, when the data is too big for the original M metric-based sequential iterative expension approach
to handle, our MapReduce version 3MA can finish in a reasonable time.
AN GROUP BEHAVIOR MOBILITY MODEL FOR OPPORTUNISTIC NETWORKS csandit
Mobility is regarded as a network transport mechanism for distributing data in many networks.
However, many mobility models ignore the fact that peer nodes often carried by people and
thus move in group pattern according to some kind of social relation. In this paper, we propose
one mobility model based on group behavior character which derives from real movement
scenario in daily life. This paper also gives the character analysis of this mobility model and
compares with the classic Random Waypoint Mobility model.
Scalable Local Community Detection with Mapreduce for Large NetworksIJDKP
Community detection from complex information networks draws much attention from both academia and
industry since it has many real-world applications. However, scalability of community detection algorithms
over very large networks has been a major challenge. Real-world graph structures are often complicated
accompanied with extremely large sizes. In this paper, we propose a MapReduce version called 3MA that
parallelizes a local community identification method which uses the $M$ metric. Then we adopt an
iterative expansion approach to find all the communities in the graph. Empirical results show that for large
networks in the order of millions of nodes, the parallel version of the algorithm outperforms the traditional
sequential approach to detect communities using the M-measure. The result shows that for local community
detection, when the data is too big for the original M metric-based sequential iterative expension approach
to handle, our MapReduce version 3MA can finish in a reasonable time.
Community detection of political blogs network based on structure-attribute g...IJECEIAES
Complex networks provide means to represent different kinds of networks with multiple features. Most biological, sensor and social networks can be represented as a graph depending on the pattern of connections among their elements. The goal of the graph clustering is to divide a large graph into many clusters based on various similarity criteria’s. Political blogs as standard social dataset network, in which it can be considered as blog-blog connection, where each node has political learning beside other attributes. The main objective of work is to introduce a graph clustering method in social network analysis. The proposed Structure-Attribute Similarity (SAS-Cluster) able to detect structures of community, based on nodes similarities. The method combines topological structure with multiple characteristics of nodes, to earn the ultimate similarity. The proposed method is evaluated using well-known evaluation measures, Density, and Entropy. Finally, the presented method was compared with the state-of-art comparative method, and the results show that the proposed method is superior to the comparative method according to the evaluations measures.
1. Basics of Social Networks
2. Real-world problem
3. How to construct graph from real-world problem?
4. What graph theory problem getting from real-world problem?
5. Graph type of Social Networks
6. Special properties in social graph
7. How to find communities and groups in social networks? (Algorithms)
8. How to interpret graph solution back to real-world problem?
Evolving social data mining and affective analysis Athena Vakali
Evolving social data mining and affective analysis methodologies, framework and applications - Web 2.0 facts and social data
Social associations and all kinds of graphs
Evolving social data mining
Emotion-aware social data analysis
Frameworks and Applications
Clustering in Aggregated User Profiles across Multiple Social Networks IJECEIAES
A social network is indeed an abstraction of related groups interacting amongst themselves to develop relationships. However, toanalyze any relationships and psychology behind it, clustering plays a vital role. Clustering enhances the predictability and discoveryof like mindedness amongst users. This article’s goal exploits the technique of Ensemble Kmeans clusters to extract the entities and their corresponding interestsas per the skills and location by aggregating user profiles across the multiple online social networks. The proposed ensemble clustering utilizes known K-means algorithm to improve results for the aggregated user profiles across multiple social networks. The approach produces an ensemble similarity measure and provides 70% better results than taking a fixed value of K or guessing a value of K while not altering the clustering method. This paper states that good ensembles clusters can be spawned to envisage the discoverability of a user for a particular interest.
Fuzzy AndANN Based Mining Approach Testing For Social Network AnalysisIJERA Editor
Fast and Appropriate Social Network Analysis (SNA) tools ,techniques, are required to collect and classify
opinion scores on social networksites , as a grouping on wrong opinion may create problems for a society or
country . Social Network Analysis (SNA) is popular means for researcher as the number of users and groups
increasing day by day on that social sites , and a large group may influence other.In this paper, we
recommendhybrid model of opinion recommendation systems, for single user and for collective community
respectively, formed on social liking and influence network theory. By collecting thedata of user social networks
and preferenceslike, we designed aimproved hybrid prototype to imitate the social influence by like and sharing
the information among groups.The significance of this paper to analyze the suitability of ANN and Fuzzy sets
method in a hybrid manner for social web sites classifications, First, we intend to use Artificial Neural
Network(ANN)techniques in social media data classification by using some contemporary methods different
than the conventional methods of statistics and data analysis, in next we want to propagate the fuzzy approach
as a way to overcome the uncertainity that is always present in social media analysis . We give a brief overview
of the main ideas and recent results of social networks analysis , and we point to relationships between the two
social network analysis and classification approaches .This researchsuggests a hybrid classification model build
on fuzzy and artificial neural network (HFANN). Information Gain and three popular social sites are used to
collect information depicting features that are then used to train and test the proposed methods . This neoteric
approach combines the advantages of ANN and Fuzzy sets in classification accuracy with utilizing social data
and knowledge base available in the hate lexicons.
Anonymization of centralized and distributed social networks by sequential cl...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Community detection from research papers (AAN dataset) using the algorithms:
K-Means
Louvain
Newman-Girvan
github link to code: https://goo.gl/CXej44
github link to project web page: http://goo.gl/7OOkhI
youtube link to video:https://goo.gl/SCpamf
dropbox link to ppt report video: https://goo.gl/cgACzU
The problem of matchmaking in electronic social networks is formulated as an optimization problem.
In particular, a function measuring the matching degree of fields of interest of a search profile with
those of an advertising profile is proposed.
SCALABLE LOCAL COMMUNITY DETECTION WITH MAPREDUCE FOR LARGE NETWORKSIJDKP
Community detection from complex information networks draws much attention from both academia and
industry since it has many real-world applications. However, scalability of community detection algorithms
over very large networks has been a major challenge. Real-world graph structures are often complicated
accompanied with extremely large sizes. In this paper, we propose a MapReduce version called 3MA that
parallelizes a local community identification method which uses the $M$ metric. Then we adopt an
iterative expansion approach to find all the communities in the graph. Empirical results show that for large
networks in the order of millions of nodes, the parallel version of the algorithm outperforms the traditional
sequential approach to detect communities using the M-measure. The result shows that for local community
detection, when the data is too big for the original M metric-based sequential iterative expension approach
to handle, our MapReduce version 3MA can finish in a reasonable time.
AN GROUP BEHAVIOR MOBILITY MODEL FOR OPPORTUNISTIC NETWORKS csandit
Mobility is regarded as a network transport mechanism for distributing data in many networks.
However, many mobility models ignore the fact that peer nodes often carried by people and
thus move in group pattern according to some kind of social relation. In this paper, we propose
one mobility model based on group behavior character which derives from real movement
scenario in daily life. This paper also gives the character analysis of this mobility model and
compares with the classic Random Waypoint Mobility model.
Sub-Graph Finding Information over Nebula Networksijceronline
Social and information networks have been extensively studied over years. This paper studies a new query on sub graph search on heterogeneous networks. Given an uncertain network of N objects, where each object is associated with a network to an underlying critical problem of discovering, top-k sub graphs of entities with rare and surprising associations returns k objects such that the expected matching sub graph queries efficiently involves, Compute all matching sub graphs which satisfy "Nebula computing requests" and this query is useful in ranking such results based on the rarity and the interestingness of the associations among nebula requests in the sub graphs. "In evaluating Top k-selection queries, "we compute information nebula using a global structural context similarity, and our similarity measure is independent of connection sub graphs". We need to compute the previous work on the matching problem can be harnessed for expected best for a naive ranking after matching for large graphs. Top k-selection sets and search for the optimal selection set with the large graphs; sub graphs may have enormous number of matches. In this paper, we identify several important properties of top-k selection queries, We propose novel top–K mechanisms to exploit these indexes for answering interesting sub graph queries efficiently.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, Twitter…etc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGIJwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, Twitter…etc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share
their interests without being at the same geographical location. With the great and rapid growth of Social
Media sites such as Facebook, LinkedIn, Twitter...etc. causes huge amount of user-generated content.
Thus, the improvement in the information quality and integrity becomes a great challenge to all social
media sites, which allows users to get the desired content or be linked to the best link relation using
improved search / link technique. So introducing semantics to social networks will widen up the
representation of the social networks.
Delta-Screening: A Fast and Efficient Technique to Update Communities in Dyna...Subhajit Sahu
Highlighted notes during research with Prof. Dip Sankar Banerjee, Prof. Kishore Kothapalli:
Delta-Screening: A Fast and Efficient Technique to Update Communities in Dynamic Graphs.
https://ieeexplore.ieee.org/document/9384277
There are 3 types of community detection methods:
Divisive, Agglomerative, and Multi-level (usually better).
In this paper, heuristics for skipping out most likely unaffected vertices for a modularity-based community detection method like Louvain and SLM (Smart Local Moving) is given. All edge batches are undirected, and sorted by source vertex id. For edge additions, source vertex i, highest modularity changing edge vertex j*, i's neighbors, and j*'s community are marked as affected. For edge deletions, where i and j must be in the same community, i, j, i's neighbors, and i's community are marked as affected. Performance is compared with static, dynamic baseline (incremental), and this method (both Louvain and SLM). Comparison is also done with "DynaMo" and "Batch" community detection methods.
For non-grid 3D images like point clouds and meshes, and inherently graph-based data.
Inherently graph-based data include for example brain connectivity analysis, scientific article citation networks, (social) network analysis, etc.
Alternative download link:
https://www.dropbox.com/s/2o3cofcd6d6e2qt/geometricGraph_deepLearning.pdf?dl=0
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
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How can social media network analysis help you get your message out? Use network maps of social media to identify the most influential contributors based on their location within the network. Use content analysis to identify the topics, hashtags and URLs of greatest interest to the "mayors" of the hashtags that matter to you.
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Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
2011 IEEE Social Computing Nodexl: Group-In-A-Box
1. Group-In-a-Box Layout for Multi-faceted Analysis
of Communities
Eduarda Mendes Rodrigues*, Natasa Milic-Frayling †, Marc Smith‡, Ben Shneiderman§, Derek Hansen¶
*
Dept. of Informatics Engineering, Faculty of Engineering, University of Porto, Portugal
eduardamr@acm.org
†
Microsoft Research, Cambridge, UK
natasamf@microsoft.com
‡
Connected Action Consulting Group, Belmont, California, USA
marc@connectedaction.net
§
Dept. of Computer Science & Human-Computer Interaction Lab
University of Maryland, College Park, Maryland, USA
ben@cs.umd.edu
¶
College of Information Studies & Center for the Advanced Study of Communities and Information
University of Maryland, College Park, Maryland, USA
dlhansen@umd.edu
Abstract—Communities in social networks emerge from One particularly important aspect of social network
interactions among individuals and can be analyzed through a analysis is the detection of communities, i.e., sub-groups of
combination of clustering and graph layout algorithms. These individuals or entities that exhibit tight interconnectivity
approaches result in 2D or 3D visualizations of clustered among the wider population. For example, Twitter users who
graphs, with groups of vertices representing individuals that regularly retweet each other’s messages may form cohesive
form a community. However, in many instances the vertices groups within the Twitter social network. In a network
have attributes that divide individuals into distinct categories visualization they would appear as clusters or sub-graphs,
such as gender, profession, geographic location, and similar. It often colored distinctly or represented by a different vertex
is often important to investigate what categories of individuals
shape in order to convey their group identity.
comprise each community and vice-versa, how the community
structures associate the individuals from the same category.
In addition to the clusters that emerge from the network
Currently, there are no effective methods for analyzing both structure, individuals in a social network are often divided
the community structure and the category-based partitions of into categories that reflect specific attributes. For example,
social graphs. We propose Group-In-a-Box (GIB), a meta- members of the Twitter community may be categorized
layout for clustered graphs that enables multi-faceted analysis based on the number of followers they have, the location
of networks. It uses the treemap space filling technique to from which they tweet, or the date they joined Twitter. Such
display each graph cluster or category group within its own attributes may be useful to gain further insights about the
box, sized according to the number of vertices therein. GIB community. Thus, in addition to detecting communities
optimizes visualization of the network sub-graphs, providing a based on the network structure, it is important to enable
semantic substrate for category-based and cluster-based analysis of the social graph along various attributes of the
partitions of social graphs. We illustrate the application of GIB individuals. At the same time, none of the widely adopted
to multi-faceted analysis of real social networks and discuss layouts for visualizing clustered graphs accommodate such
desirable properties of GIB using synthetic datasets. multi-faceted analysis.
Our research fills that gap by extending the work on
Keywords—network visualization; group-in-a-box; layout; network visualization with semantic substrates [20]. We
meta-layout; force-directed; communities; clustering; semantic introduce Group-In-a-Box (GIB), a meta-layout for clustered
substrates.
graphs that enables multi-faceted analysis of networks. GIB
uses the treemap space filling technique to create graphs for
I. INTRODUCTION individual categories, thus providing a semantic substrate for
Network structures appear in many contexts, from laying out each group. Furthermore, we demonstrate the use
biological systems to communications networks. With the of the method to analyze communities, i.e., graph clusters by
recent proliferation of social media services such as Twitter assigning them specific spatial regions, applying layout
and Facebook, public awareness and usage of social network algorithms to reveal the local cluster structure, and observing
data have increased. This led to a growing need for the categories that their members fall into. In both cases, we
comprehensible visualizations of complex networks that arrive at well separated sub-graphs that clearly reveal the
enable exploratory analysis.
2. intra-group network structures and the attributes of their where such flows primarily follow the shortest available
members. path.
Our experiments illustrate the application of the GIB The Clauset-Newman-Moore (CNM) algorithm [3], on
multi-faceted analysis to a social network that arises from the the other hand, is a hierarchical agglomeration algorithm for
voting activities of U.S. Senators. Additionally, we use detecting community structure, which is computationally
synthetic networks, generated according to pseudo-random, more efficient than Girvan-Newman and similar algorithms.
small world and preferential attachment network models, to Its running time for a network with n vertices and m edges is
demonstrate that the GIB layout exhibits desirable properties O(md log n), where d is the depth of the hierarchy. However,
for visualizing vertices that connected different clusters. In in practice, the application of CNM is limited to medium-
social networks such individuals are often referred to as size networks, up to half a million vertices. Wakita and
boundary spanners and serve as connectors, forming Tsurumi [23] show that this inefficiency is caused by
communication pathways through which information can be merging communities in an unbalanced manner. They
exchanged across communities. Thus, GIB is equally useful propose simple heuristics to achieve a balanced merge and
for gaining insights about the role of boundary spanners scale up to social networks with millions of users.
within their own community. Based on our experiments, we
recommend generalization of the GIB meta-layout to B. Graph Layouts
accommodate multi-faceted analysis involving hierarchical A common approach to visualizing networks is to use
classification schema and hierarchical clustering algorithms. force-directed layout of vertices and edges based on models
In the following sections, we introduce the graph of physical systems in order to arrive at optimal graph
clustering and layout algorithms used in our study and structures. The Fruchterman-Reingold (FR) layout [9]
describe the GIB layout in detail. Through empirical belongs to that class of algorithms. It treats vertices as steel
observations of specific datasets and network visualization rings and edges as springs between them, observing two
methods, we explore various aspects of the multi-faceted forces: an attractive force that is analogous to a spring force
analysis. We conclude with the discussion of related work abiding by Hooke’s law and a repulsive force that acts as an
and guidelines for devising algorithms for multi-faceted electrical force between charged particles, similar to
analyses, including the need for readability criteria to Coulomb’s law. The algorithm minimizes the energy of the
optimize the utility of the GIB-like layouts. system by moving the vertices and changing the forces
between them until the system reaches an equilibrium state.
II. BACKGROUND AND PROBLEM DEFINITION This layout algorithm is useful for visualizing large
Community detection algorithms aim to identify undirected networks and, generally, creates overall satisfying
cohesive, tightly connected sub-graphs within a network. layouts, placing vertices of the same cluster in the proximity
These structural clusters can be analyzed by considering both of each other. However, some local areas of the graph may
intra-cluster and inter-cluster links. Next, we introduce the still be sub-optimally laid out.
graph clustering and layout algorithms that we used in our Harel and Koren (HK) proposed a fast multi-level graph
experiments and analysis. layout algorithm to achieve better visualizations [11]. Their
A. Detecting Communities through Network Clustering approach involves a two-phase method that recursively
coarsens the graph to arrive at its multi-level representation.
The notion of a community refers to a subset of vertices First, the graph is embedded in a high dimensional space and
among which the distribution of edges is denser than with then projected onto a 2-D plane using principal components
other vertices in the network. There are numerous methods analysis.
for detecting communities, including hierarchical clustering,
graph partitioning by maximizing specific criteria such as C. Faceted Analysis of Communities
network modularity, and similar. In practice, social network analysis (SNA) is multi-
The Girvan–Newman algorithm [7] takes a divisive faceted. Often it is important to analyze the roles of
hierarchical approach. It uses edge betweenness as weights in individuals that arise from their interaction within and across
the divisive process. Community boundaries are detected by communities. However, in order to gain further insights,
progressively removing from the network the edges with the SNA may explore additional, non-structural attributes that
highest betweenness, re-calculating the betweenness of the are not considered by the clustering algorithm. Indeed, the
remaining edges at each step. If a network contains loosely individuals may belong to different categories based on
connected communities, e.g., linked by a few inter-cluster gender, profession, age, location, etc. It is often useful to
edges, then all shortest paths must pass through one of these observe the distribution of these attributes across the
few edges. Thus, such inter-cluster edges will have high community structure. However, to support the analysis that
betweenness and the associated vertices will have high vertex takes into account both the detected community structure and
betweenness centrality [8], a useful measure of the vertex’ the categories that individual belong to, we need to address
influence on the information flow, especially in networks two specific issues:
3. (a) (b)
Figure 1. (a) Harel-Koren (HK) fast multi-scale layout of a clustered network of Twitter users, using color to differentiate among the vertices in different
clusters. The layout produces a visualization with overlapping cluster positions. (b) Group-in-a-Box (GIB) layout of the same Twitter network: clusters are
distributed in a treemap structure that partitions the drawing canvas based on the size of the clusters and the properties of the rendered layout. Inside each
box, clusters are rendered with the HK layout.
1) There are no layouts that enable flexible visualizations of represent trees through nested rectangular regions. A
categories and links across categories that arise from the rectangular area is subdivided into a set of rectangles that
underlying network structure. represent the top level vertices in the tree hierarchy. This
process continues recursively, creating rectangles that
2) Clustering and layout algorithms are typically applied represent each level in the tree through alternate vertical and
separately. As a result, the graph layout does not take into horizontal subdivision of the rectangles.
account cluster membership of vertices and causes two GIB was implemented as part of the NodeXL network
effects: (i) occlusion of clusters within the graph layout and analysis tool 1 . NodeXL supports computation of network
(ii) loss of information about the structure of individual metrics, such as closeness and betweenness centrality, and
clusters and inter-cluster connectivity. mapping of vertex properties onto visual characteristics, such
The former is illustrated in Figures 1 (a) and Figure 6 (a) as size, color, and shape of the vertices [10][21]. We use
where the CNM clustering algorithm is combined with the NodeXL to generate all the graphs presented in the paper.
HK layout. The clusters overlap, as indicated through ‘color
bleeding’ throughout the network visualization. IV. GIB METHOD FOR MULTI-FACETED ANALYSIS
In this section we demonstrate the use of GIB in multi-
III. GIB LAYOUT ALGORITHM faceted analysis of the U.S. Senate voting patterns in 20072.
We propose to support multi-faceted network analysis by
generalizing the approach of network visualization with A. Dataset
semantic substrates [20]. We present the Group-In-a-Box The U.S. Senate co-voting network was created from the
(GIB) layout that simultaneously captures clustering and data that connects senators to one another based on the
categorization of vertices in a network graph. The GIB number of times they voted the same way (i.e., both in favor
algorithm, or both against a bill). The network graph includes 98
- Uses the treemap space filling technique [1], [15], [19] to vertices and 9506 weighted edges, each representing the
partition the graph canvas into regions of varying sizes, percentage of voting agreement between a pair of senators.
in which individual clusters or category groups are The graph is complete, i.e., all the senators are connected to
displayed. each other. In order to capture strong relationships among the
senators, we focus on edges between senators whose level of
- Allows a choice of layout algorithms for optimizing the agreement is above 50%. The dataset also includes attributes
layout of the sub-graphs within each region. about the senators, such as name, party affiliation, the state
- Enables assignment of visual properties to vertices and they represent, and the number of votes.
edges within and across clusters and category groups.
We choose the treemap approach for the rich information
it conveys about the relative size of individual clusters or 1
NodeXL is freely available at http://nodexl.codeplex.com.
categories (e.g., see Figure 1 (b)) and for its extensibility to 2
Data originally provided by Chris Wilson of Slate magazine available in
hierarchical structures. Indeed, treemaps are designed to the NodeXL template format at http://casci.umd.edu/NodeXL_Teaching.
4. (a) (b)
Figure 2. The 2007 U.S. Senate co-voting network graph, obtained with the Fruchterman-Reingold (FR) layout. Vertices colors represent the senators’
party affiliations (blue: Democrats; red: Republicans; orange: Independent) and their size is proportional to betweenness centrality. Edges represent
percentage of agreement between senators: (a) above 50%; (b) above 90%.
(a) (b)
Figure 3. The 2007 U.S. Senate co-voting network graph, visualized with the GIB layout. The group in each box represents senators from a given U.S.
region (1: South; 2: Midwest; 3: Northeast; 4: Mountain; 5: Pacific) and individual groups are displayed using the FR layout. Vertices colors represent the
senators’ party affiliations (blue: Democrats; red: Republicans; orange: Independent) and their size is proportional to betweenness centrality. Edges
represent percentage of agreement between senators: (a) above 50%; (b) above 90%..
B. GIB Application to U.S. Senate Co-voting Network by larger vertices), one may want to explore additional facets
The FR layout of the U.S. Senate co-voting network of the data to gain further insights about the voting patterns.
yields the graph shown in Figure 2 (a). In this visualization, We use GIB to perform such analysis. First, we group the
the vertices are colored according to the senators’ party States into five regions: Northeast, Midwest, South,
affiliation and sized according to the vertex betweenness Mountain, and Pacific. We then use these regions to divide
centrality. Further filtering of the edges, to retain those with senators into five groups, one group per region. Using the
weight above 90%, yields the graph shown in Figure 2 (b), same edge filtering thresholds as before, the GIB layout
showing a strong voting agreement among the senators of the results in Figure 3. GIB reveals further information about the
Democratic party. cohesiveness of the two parties across different U.S. regions.
Although this analysis already reveals interesting facts For example, the Republican senators exhibit more voting
about the party cohesion and boundary spanners (represented cohesion in the southern states than the Democrats. Such
observation was not possible in the full graph visualization.
5. (a) (b) (c)
Figure 4. Small-world network graph visualization obtained with the Harel-Koren layout, after clustering the graph with the Clauset-Newman-Moore
community detection algorithm (5 clusters). (a) Full graph with 500 vertices colored according to the cluster membership. (b) GIB showing the structural
properties of the individiual clusters. (c) GIB layout of the same 5 clusters showing inter-cluster edges.
V. GIB APPLICATION TO COMMUNITY CLUSTERS its own box, increasing the visibility of intra-cluster (b) and
The GIB layout can also be applied to gain further inter-cluster (c) edge structures. GIB provides clarity
insights into clustered graphs, in particular about the regarding the regular clique structures of the local
boundary spanners whose position within the original graphs neighborhoods within each cluster. It also enables the
can be obscured due to cluster occlusion. By generating identification of vertices with “long range” edges,
synthetic network datasets, with known structural properties, responsible for shrinking the network diameter.
we illustrate the conditions under which the GIB layout leads C. GIB Visualization of Pseudo-random Graphs
to new insights.
The pseudo-random graphs containing synthetic clusters
A. Datasets do not exhibit any particularly interesting intra-cluster
We used the CONTEST Matlab toolbox [22] to generate structure to analyze. However, they enable us to explore
the following synthetic network datasets: limitations regarding the visualization of clustered graphs for
- Small-world network with 500 vertices (avg. shortest- a range of inter-cluster edge probabilities. Additionally, they
path length = 10.349; clustering coefficient = 0.467), enable us to observe the positioning of boundary spanners
generated according to the Watts-Strogatz model [24]. both with and without GIB.
The visualizations in Figure 5 show two pseudo-random
- Two pseudo-random graphs, each containing 5 clusters networks with five clusters displayed using the HK fast
of different size (comprising 20, 40, 60, 80 and 100 multi-scale layout algorithm. GIB is used to isolate each of
vertices), generated according to the Erdös-Rényi model the five clusters into its own box. Figures 5 (a) and (b)
[6], with intra-cluster edge probability of 0.15. The two illustrate the effect of increasing the inter-edge density on the
graphs differ in the inter-cluster edge probability set to occlusion of the clusters and the re-positioning of boundary
0.005 and 0.05, respectively. spanners (i.e. the vertices with the largest size), which tend
to gravitate towards the periphery of the clusters, when these
- Scale-free network comprising 500 vertices, generated
are more separated. The GIB layouts, shown in Figures (c)
using the Barabási and Albert preferential attachment
and (d), display essentially the same intra-cluster structure
model [2]. Each new vertex was given 3 edges on arrival,
for both graphs. This is expected since both have the same
leading to a scale-free degree distribution.
intra-cluster edge probability of 0.15. In this case, GIB
enables a clear visualization of the centrality of boundary
B. GIB Visualization of a Small-world Network spanners within their own community.
Small-world networks are characterized by high clustering
coefficients and small average shortest path between pairs of D. GIB Visualization of a Scale-free Network
vertices. Vertices in such network are densely connected The scale-free network shown in Figure 6 (a) models
with only a small number of neighbors, but can be reached structural properties of real social networks with power-law
from any other vertex in the network through a short path. degree distributions. The CNM algorithm identified 10
The HK layout of the clustered small world network shown clusters in this network. The color of the vertices reflects the
in Figure 4 (a) while enabling the observation of some cluster membership while the size corresponds to the vertex
structural regularity, it obfuscates the details about the betweenness centrality. By removing the boundary edges and
structure of individual clusters detected by the CNM applying the HK layout algorithm we arrive at the graph (b),
algorithm. On the other hand, the GIB visualizations shown which renders separate clusters on top of each other.
in Figures 4 (b) and (c) isolate each of the five clusters into
6. (a) (b)
(c) (d)
Figure 5. Pseudo-random graphs with 5 clusters of different sizes (comprising 20, 40, 60, 80 and 100 vertices), with intra-cluster edge probability of 0.15:
(a) inter-cluster edge probability of 0.005; (b) inter-cluster edge probability of 0.05. The graphs are visualized using the Harel-Koren fast multi-scale layout
agorithm and vertices are sized by betweenness centrality. The visualizations in (c) and (d) are the corresponding GIB layout.
The GIB algorithm provides an improved and clearer boundaries, GIB adds value by showing the local, within-
view of the structure of individual clusters. It also shows that group link structure and inter-linking of the groups.
vertices with high betweenness play a central role within Our method of assessing the effectiveness of the GIB
individual clusters and are pulled towards the center of the layout is based on observations how well GIB reveals prior
clusters. This is expected in networks generated through knowledge about the networks. We also consider how well it
preferential attachments since such vertices also exhibit high abides by the design principle proposed by Krempel [17]:
closeness centrality. “constrain the solutions to simple patterns, patterns which
are known to put relatively little demand on the perceptual
VI. DISCUSSION AND RELATED WORK skills of an audience.” In that vein we considered relatively
standard notions and characteristics of networks that the
A. Observations from the GIB Experiments users are likely to observe and interpret by themselves.
Investigation of GIB across real and synthetic networks Experiments with the synthetic networks show that GIB
revealed several properties. First, GIB layout subsumes three identifies and confirms known properties of specific graphs:
important aspects in the visualization of networks: (1) it - The layout of small world networks highlights the long-
enables simultaneous filtering or grouping of vertices based range links between remote vertices. From inter-cluster
on a single or multiple attributes, (2) it enables layout links one can observe the cross-cluster effects of remote
optimization of individual groups while maintaining visual vertices (Figure 4).
awareness of other groups, and (3) it supports explicit - The layout of random networks shows boundary spanners
connection among close and remote vertices across groups. pulled centrally towards the direction of other clusters.
While some of these aspects can be achieved by simple Locally, within their own cluster, they are uniformly
filtering, the unifying effect that GIB offers is unique and not distributed, as expected. That is the case, both when the
easily achieved by other methods. links across clusters are sparse and when they are dense
Furthermore, with the focus on the local optimization of (Figure 5).
the group structure, GIB is robust and applicable across - In scale-free preferential attachment networks, the GIB
networks with different clusterability. Indeed, even in the local structure clearly highlights the presence of vertices
case of graph visualizations without clearly discerned cluster with both high closeness and betweenness centrality. This
7. (a) (b) (c)
(d) (e)
Figure 6. Scale-free network with 10 clusters detected by the Clauset-Newman-Moore algorithm. Vertices are colored by cluster membership and sized by
betweeness centrality. (a) Harel–Koren layout of the clustered graph. (b) Harel–Koren layout after removing inter-cluster edges. (c) Fruchterman-Reingold
layout after removing inter-cluster edges. (d) GIB showing inter-cluster edges and (e) GIB showing intra-clsuter edges.
is a defining property of such networks, giving advantage this sense, GIB expands the range of scenarios in which
to vertices and groups that form earlier in the network semantic substrates can be applied.
lifecycle (Figure 6). Harrer et al. [12] discuss various analyses of complex
Thus, for a range of analysis scenarios, from multi- networks and illustrate how rich semantic underpinning,
attribute characterization of networks to consistent insights such as ontologies that support user interaction around the
about network properties, GIB appears as a promising tool knowledge base, can lead to more complex network
for analyzing complex networks. representations. In such cases, one is likely to see a shift in
the network modeling, e.g., from a bi-partite graph analysis
B. Related Work on Multi-facted Network Analysis to multi-mode networks where more complex interactions
In their work on semantic substrates, Shneiderman and need to be expressed. In this view, the GIB layout covers a
Aris [20] promote two principles: (1) the use of semantic limited set of scenarios, where attributes themselves do not
substrates that are user-defined and (2) the use of controls, induce additional structure that needs to be taken into
such as sliders, to mediate the visibility and clutter in account. Moving towards a hierarchical representation of
visualization and ensure comprehensibility of the network categories would be the first step in that direction.
graphs. We extend this approach to semantic groupings of
nodes that can be expressed by a single or multiple attributes, C. Related Work on Layout of Clustered Graphs
as in the U.S. Senate voting analysis (Figures 2 and 3). We Clutter in network visualization may occur due
show that with GIB the user can explore semantic substrates positioning of the vertices or through interaction with the
that are based on either structural clusters or attribute-based graph, e.g., by exposing labels on vertices or edges. This
groupings of graph vertices. These, in turn, can be further issue has been addressed by modifying the original layout to
overlaid with additional attributes associated with vertices. In improve the readability. However, such transformations of
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