The Purpose and Process of PhD Comprehensive VIVA Examination (IIIT Hyderabad) : NOTES
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Highlighted notes on: The Purpose and Process of PhD Comprehensive VIVA Examination (IIIT Hyderabad) After clearing breadth qualifier (while doing depth courses), student needs to do literature survey deeply on 8-10 research papers (needs to be forwarded to advisory committee), along with studying other related papers. Advisory committee is your advisor, a subject matter expert, and another member outside of the area of research. Student is given a set of questions beforehand based on selected papers. A report (6-10 pages) and presentation needs to be prepared before the comprehensive viva.
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This document provides guidelines for writing Master's and Doctoral theses at Middle East Technical University's Faculty of Architecture, Department of City and Regional Planning. It outlines the standard major headings and minimum subheadings that should be included in a thesis proposal, thesis, and literature review. The proposal must introduce the research problem and question, review relevant literature, and propose a methodology. The thesis should include sections on introduction, literature review, methodology, findings, and conclusions. The literature review aims to demonstrate the researcher's expertise and show how previous work informs their study.
TYPE TITLE OF DISSERTATION IN ALL UPPERCASE LETTERS USING TWO-TIER.docx
TYPE TITLE OF DISSERTATION IN ALL UPPERCASE LETTERS USING TWO-TIERED LINES LIMITING TEXT TO TWELVE WORDS
Dissertation
Submitted in partial fulfillment
of the requirements for the degree of Doctor of Education
in the Carter and Moyers School of Education
at Lincoln Memorial University
by
Jane B. Doe
May 2018
ii
© 2018
Jane B. Doe
All Rights Reserved
Dedication
This page is optional. If a candidate wants to dedicate the dissertation to a particular person, the candidate should limit the dedication to one or two sentences.
(Note that all preliminary pages are numbered with Roman numerals. If included, the Dedication page is numbered page iii.)
v
vii
Acknowledgments
The acknowledgments page is used to recognize those who have helped in the official dissertation process including persons who granted permission to use copyrighted material, those who provided grants and special funding for the project, and guidance of the dissertation Chairperson and dissertation committee. If a candidate has permission to use data from a longitudinal study, the candidate should recognize the author(s) of that study in the acknowledgments section. The candidate should not recognize peer reviewers, editors, family, friends, or classmates in the acknowledgments page. Personal comments belong on the dedication page. Acknowledgments should be brief (one indented paragraph), with text not exceeding 150 words.
Abstract
The abstract should be one paragraph with no indentation but a ragged right edge. The abstract should be a single, double-spaced paragraph with a range of 120 to 150 words. Candidates should avoid using direct quotes and references or citations in the abstract. Candidates should also avoid the use of acronyms in the abstract. Candidates should briefly state the problem, the population or sample size, one major finding or result, and a concluding statement or generalization.
Table of Contents
CHAPTER PAGE
Chapter I: Introduction1
Statement of the Problem1
Research Questions1
Theoretical Framework2
Significance of the Project3
Definition or Description of the Terms3
Assumptions of the Study4
Chapter II: Review of the Literature5
Level-2 Heading5
Level-2 Heading6
Level-2 Heading7
Level-2 Heading7
Chapter III: Methodology8
Research Design8
Population of the Study9
Data Collection9
Analytical Methods9
Reliability and Validity10
CHAPTER PAGE
Limitations and Delimitations10
Chapter IV: Analyses and Results12
Data Analysis12
Research Questions12
Summary of Results14
Chapter V: Conclusions and Recommendations15
Conclusions of the Study15
Recommendations of the Study16
Implications of the Study17
References18
Appendix A Lincoln Memorial University Dissertation Guidelines19
Appendix B Guidelines for Appendices22
Use this table of contents (TOC) as an example of what one looks like. When it comes time for creating your own TOC, RIGHT CLICK anywhere in the Table of Contents, select UPDATE FIELD, then select UPDATE ENTIRE TABLE or UPDA ...
Proposal writing
This document provides guidance on writing a thesis proposal, outlining its typical structure and content. It discusses that a proposal justifies and plans a research project and demonstrates an understanding of how to conduct discipline-specific research. The proposal format generally includes a cover page, table of contents, introduction, literature review, materials and methodology, expected results and discussions, conclusions, references, and appendices. Each section is described in detail to guide students in developing their proposals.
Chapter 3.pptx
This document provides an overview of the key components and structure of a research proposal. It discusses the purpose of a research proposal as a means to communicate a research plan and obtain funding or authorization for a project. The document then outlines the typical sections included in a research proposal, such as the title page, abstract, introduction/background, literature review, methodology, timeline, budget, and references. It provides guidance on what to include in each section, with an emphasis on clearly presenting the problem statement, objectives, methodology, and significance of the study. Overall, the document serves as a guide for developing a well-organized research proposal that follows standard conventions.
Write a scholarly research report on a topic related to Advanced.docx
Write a scholarly research report on a topic related to Advanced Database Systems (see
Appropriate Topics
). Please see
Important Notes
and
Document Details
for detailed specifications.
Appropriate Topics:
The Research Report, select one of the following research areas:
1. A Complete Comparative between MongoDB and Oracle 12c
2. A Complete Comparative between IBM DB2 and CA IDMS
3. A Complete Comparative between IBM DB2 and IBM DB2
4. A Complete Comparative between Oracle 12c and CA IDMS
5. A Complete Comparative between Oracle 12c and IBM DB2
6. A Comparative Analysis of Triggers used in Oracle 12c versus Triggers used IBM DB2
7. A Comparative Analysis of Stored Procedures used in Oracle 12c versus Stored Procedures used in IBM DB2
8. A Comparative Analysis of Transaction Processing used in Oracle 12c versus Transaction Processing used IBM DB2
9. A study of DB Transaction Processing / Coordination used in a Cloud environment
10. Common and dissimilar vulnerabilities found in both Oracle 12c and IDMS
11. Managing Transaction Processing using MongoDB
12. The Internet of Things (sensor & actuator data) used in a distributed DB cloud environment
Important Student Notes:
·
Each student submission should be checked for plagiarism.
Students should be warned that Turnitin has a very good historical memory and is capable of accessing reports from both internal and external resources (i.e. Universities, Governments, etc.) including those originally written in non-English written languages. Plagiarism will result in a grade of zero (non-negotiable) for the assignment and may results in other university actions. The department chairperson will be notified of the violation. Additional Campbellsville University penalties may be applicable. Please see class syllabus for additional details.
· Only one submission attempt is permitted – AS THE STUDENT TO BE SURE BEFORE DEPRESSING ENTER.
· Acceptable file formats for submissions include Microsoft Word (doc, docx) or Adobe Acrobat (PDF). No other formats are acceptable.
· The research paper must be at least 3,500 words supported by evidence (citations from peer-reviewed sources).
· A minimum of four (4) peer-reviewed journal citations are required.
· Formatting should be double-spaced, one-inch boarders, no extra space for headings, no extra white space, no more than two levels of heading, page numbers, front and back matter).
· Extra white space use to enhance page count will negatively affect student grade.
· Chapter 1 illustrates the
document details
of the research report and constitutes Background/Introduction, Problem Statement(s), Goal(s), Research Question(s), Relevance and Significance, Barriers and Issues related to topic chosen. Chapter 2 should consist of student paraphrasing the cited research material (i.e. what happened in case study x). Chapter 3 should be the reasoning for doing a basic compare/contrast or advantages/disadvantage of what was stated in Chapt.
Appropriate TopicsThe Research Report, select one of the fo.docx
Appropriate Topics:
The Research Report, select one of the following research areas:
1. A Complete Comparative between MongoDB and Oracle 12c
2. A Complete Comparative between IBM DB2 and CA IDMS
3. A Complete Comparative between IBM DB2 and IBM DB2
4. A Complete Comparative between Oracle 12c and CA IDMS
5. A Complete Comparative between Oracle 12c and IBM DB2
6. A Comparative Analysis of Triggers used in Oracle 12c versus Triggers used IBM DB2
7. A Comparative Analysis of Stored Procedures used in Oracle 12c versus Stored Procedures used in IBM DB2
8. A Comparative Analysis of Transaction Processing used in Oracle 12c versus Transaction Processing used IBM DB2
9. A study of DB Transaction Processing / Coordination used in a Cloud environment
10. Common and dissimilar vulnerabilities found in both Oracle 12c and IDMS
11. Managing Transaction Processing using MongoDB
12. The Internet of Things (sensor & actuator data) used in a distributed DB cloud environment
Important Student Notes:
·
Each student submission should be checked for plagiarism.
Students should be warned that Turnitin has a very good historical memory and is capable of accessing reports from both internal and external resources (i.e. Universities, Governments, etc.) including those originally written in non-English written languages. Plagiarism will result in a grade of zero (non-negotiable) for the assignment and may results in other university actions. The department chairperson will be notified of the violation. Additional Campbellsville University penalties may be applicable. Please see class syllabus for additional details.
· Only one submission attempt is permitted – AS THE STUDENT TO BE SURE BEFORE DEPRESSING ENTER.
· Acceptable file formats for submissions include Microsoft Word (doc, docx) or Adobe Acrobat (PDF). No other formats are acceptable.
· The research paper must be at least 3,500 words supported by evidence (citations from peer-reviewed sources).
· A minimum of four (4) peer-reviewed journal citations are required.
· Formatting should be double-spaced, one-inch boarders, no extra space for headings, no extra white space, no more than two levels of heading, page numbers, front and back matter).
· Extra white space use to enhance page count will negatively affect student grade.
· Chapter 1 illustrates the
document details
of the research report and constitutes Background/Introduction, Problem Statement(s), Goal(s), Research Question(s), Relevance and Significance, Barriers and Issues related to topic chosen. Chapter 2 should consist of student paraphrasing the cited research material (i.e. what happened in case study x). Chapter 3 should be the reasoning for doing a basic compare/contrast or advantages/disadvantage of what was stated in Chapter 2 (do not state because the professor said so). Chapter 4 is a complete analysis, synthesis, and evaluation of what was stated in chapter 2. In effect, chapter 3 is a statement of what will .
Guideline for master's thesis evaluation
The document provides guidelines for evaluating master's theses at Helsinki University of Technology. It outlines the general objectives and requirements of a master's thesis. A thesis must demonstrate the student's skills and independent work. It will be evaluated based on how well it meets criteria in areas such as defining the research scope, commanding the topic, using appropriate methods, making an original contribution, and having a clear written presentation. Thesis supervisors are responsible for submitting evaluation reports. Five grades are defined - excellent, very good, good, satisfactory, and fail - based on the extent to which the thesis meets the basic evaluation criteria.
Examining Phdthesis Feb2010 Prof Madya Dr Najib
The document discusses the criteria that examiners use to evaluate Masters and PhD theses. It outlines several areas that examiners will examine, including research methods, presentation, analysis, significance of findings, and original contribution to the field of knowledge. Examiners seek to determine if the candidate has demonstrated a sufficient level of research and analysis skills to be awarded the degree. The possible examination results are outlined as pass, pass with minor revisions, conditional pass with major revisions, reschedule examination, award of a lower degree such as MPhil, or fail.
designing proposal for research projects budget and funding schemes
This document provides information about writing a research proposal for funding. It begins with the aim and objectives of making participants proficient in writing research proposals. It then discusses key elements like the title, introduction, literature review, methodology, expected outcomes, facilities, budget, funding agencies and eligibility. The document provides guidance on writing each section of the proposal, including defining problems, objectives, hypotheses and significance. It also outlines the budget, time schedule and reviewers' expectations for funding approval.
1 RSCH 600 Research Proposal writing RESEA
1
RSCH 600 Research Proposal writing
RESEARCH PROPOSAL - PART 1: INTRODUCTION, REVIEW OF LITERATURE, PROBLEM STATEMENT,
RESEARCH QUESTIONS
Due at the end of week 7 (Sunday February 28, 2021 at midnight)
You need to conduct some research related to your own interests or to serve the interests of some organization
(employer/client/sponsor). Before you start writing a formal proposal, you (or your employer/client/sponsor) would like to
gather some information about the problem as well as understand its importance.
Choose your research topic thoughtfully and submit the topic of your research proposal for approval the latest by the end of
Week 3 (Sunday January 31, 2021 at midnight). You can find the submission box in Week 3.
This part of your research proposal should be roughly 7-9 pages (excluding cover page, references and annex) and written
in paragraphs, report format. All citations and references for this course are to be done in the APA style.
HERE ARE THE KEY PARTS OF THIS PART OF THE PROPOSAL (PART 1):
Title of your research
Make it intriguing – (BUT LESS THAN 120 CHARACTERS)
Introduction/Background
Provide a brief description of what the proposed research topic is about, why is it important and how you came to be
interested in it.
Literature Review
The purpose of this assignment is to develop skills in finding and analyzing valid literary resources for your research.
The review should be written in an integral / synthetic style, and NOT as an annotated bibliography.
Please note: Use in-text citations to reference all ideas, concepts, text, and data that are not your own. If you make a
statement, back it up with a reference!
• Research a minimum of 15 relevant business-related literature sources (focus on material available in digital
format only for this course). Carefully choose your 15 "keepers" that are clearly related to your study. (Note: you
might need to find 30 to “keep” only 15).
• Identify major common themes encountered in the selected literature sources. For each theme, create its own
subsection within the literature review.
• For each theme / subsection of the literature review, explain the opinions of the authors and show their
similarities, differences, methods of acquisition of data, methods of data analysis and other pertinent information.
State how the reviewed research results relate to your proposed study.
• Identify the gaps in the literature and explain what needs to be done to move forward in your research.
Statement of the Problem
Detail the problem that you are considering. Explain:
• How are you defining and delimiting the specific area of the research?
• What is the gap that will be addressed by this research?
• What it is that you hope to learn by conducting this research?
• Discuss the anticipated outcomes and benefits to the researcher, the organization, or society.
Research Questions
Provide focused ...
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Bab 02 Literature Survey - Learn how to do literatur survey for research.pptx
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TYPE TITLE OF DISSERTATION IN ALL UPPERCASE LETTERS USING TWO-TIER.docx
TYPE TITLE OF DISSERTATION IN ALL UPPERCASE LETTERS USING TWO-TIER.docx
Write a scholarly research report on a topic related to Advanced.docx
Write a scholarly research report on a topic related to Advanced.docx
Appropriate TopicsThe Research Report, select one of the fo.docx
Appropriate TopicsThe Research Report, select one of the fo.docx
designing proposal for research projects budget and funding schemes
designing proposal for research projects budget and funding schemes
Prof. Lamrid Lahbib S4- Introduction to Advanced Composition.pptx
Prof. Lamrid Lahbib S4- Introduction to Advanced Composition.pptx
More from Subhajit Sahu
About TrueTime, Spanner, Clock synchronization, CAP theorem, Two-phase lockin...
TrueTime is a service that enables the use of globally synchronized clocks, with bounded error. It returns a time interval that is guaranteed to contain the clock’s actual time for some time during the call’s execution. If two intervals do not overlap, then we know calls were definitely ordered in real time. In general, synchronized clocks can be used to avoid communication in a distributed system.
The underlying source of time is a combination of GPS receivers and atomic clocks. As there are “time masters” in every datacenter (redundantly), it is likely that both sides of a partition would continue to enjoy accurate time. Individual nodes however need network connectivity to the masters, and without it their clocks will drift. Thus, during a partition their intervals slowly grow wider over time, based on bounds on the rate of local clock drift. Operations depending on TrueTime, such as Paxos leader election or transaction commits, thus have to wait a little longer, but the operation still completes (assuming the 2PC and quorum communication are working).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting Bitset for graph : SHORT REPORT / NOTES
Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is commonly used for efficient graph computations. Unfortunately, using CSR for dynamic graphs is impractical since addition/deletion of a single edge can require on average (N+M)/2 memory accesses, in order to update source-offsets and destination-indices. A common approach is therefore to store edge-lists/destination-indices as an array of arrays, where each edge-list is an array belonging to a vertex. While this is good enough for small graphs, it quickly becomes a bottleneck for large graphs. What causes this bottleneck depends on whether the edge-lists are sorted or unsorted. If they are sorted, checking for an edge requires about log(E) memory accesses, but adding an edge on average requires E/2 accesses, where E is the number of edges of a given vertex. Note that both addition and deletion of edges in a dynamic graph require checking for an existing edge, before adding or deleting it. If edge lists are unsorted, checking for an edge requires around E/2 memory accesses, but adding an edge requires only 1 memory access.
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTES
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Experiments with Primitive operations : SHORT REPORT / NOTES
This includes:
- Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
- Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
- Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
- Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
PageRank Experiments : SHORT REPORT / NOTES
This includes:
- Adjusting data types for rank vector
- Adjusting Pagerank parameters
- Adjusting Sequential approach
- Adjusting OpenMP approach
- Comparing sequential approach
- Adjusting Monolithic (Sequential) optimizations (from STICD)
- Adjusting Levelwise (STICD) approach
- Comparing Levelwise (STICD) approach
- Adjusting ranks for dynamic graphs
- Adjusting Levelwise (STICD) dynamic approach
- Comparing dynamic approach with static
- Adjusting Monolithic CUDA approach
- Adjusting Monolithic CUDA optimizations (from STICD)
- Adjusting Levelwise (STICD) CUDA approach
- Comparing Levelwise (STICD) CUDA approach
- Comparing dynamic CUDA approach with static
- Comparing dynamic optimized CUDA approach with static
Algorithmic optimizations for Dynamic Monolithic PageRank (from STICD) : SHOR...
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting OpenMP PageRank : SHORT REPORT / NOTES
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings o...
Below are the important points I note from the 2020 paper by Martin Grohe:
- 1-WL distinguishes almost all graphs, in a probabilistic sense
- Classical WL is two dimensional Weisfeiler-Leman
- DeepWL is an unlimited version of WL graph that runs in polynomial time.
- Knowledge graphs are essentially graphs with vertex/edge attributes
ABSTRACT:
Vector representations of graphs and relational structures, whether handcrafted feature vectors or learned representations, enable us to apply standard data analysis and machine learning techniques to the structures. A wide range of methods for generating such embeddings have been studied in the machine learning and knowledge representation literature. However, vector embeddings have received relatively little attention from a theoretical point of view.
Starting with a survey of embedding techniques that have been used in practice, in this paper we propose two theoretical approaches that we see as central for understanding the foundations of vector embeddings. We draw connections between the various approaches and suggest directions for future research.
DyGraph: A Dynamic Graph Generator and Benchmark Suite : NOTES
https://gist.github.com/wolfram77/54c4a14d9ea547183c6c7b3518bf9cd1
There exist a number of dynamic graph generators. Barbasi-Albert model iteratively attach new vertices to pre-exsiting vertices in the graph using preferential attachment (edges to high degree vertices are more likely - rich get richer - Pareto principle). However, graph size increases monotonically, and density of graph keeps increasing (sparsity decreasing).
Gorke's model uses a defined clustering to uniformly add vertices and edges. Purohit's model uses motifs (eg. triangles) to mimick properties of existing dynamic graphs, such as growth rate, structure, and degree distribution. Kronecker graph generators are used to increase size of a given graph, with power-law distribution.
To generate dynamic graphs, we must choose a metric to compare two graphs. Common metrics include diameter, clustering coefficient (modularity?), triangle counting (triangle density?), and degree distribution.
In this paper, the authors propose Dygraph, a dynamic graph generator that uses degree distribution as the only metric. The authors observe that many real-world graphs differ from the power-law distribution at the tail end. To address this issue, they propose binning, where the vertices beyond a certain degree (minDeg = min(deg) s.t. |V(deg)| < H, where H~10 is the number of vertices with a given degree below which are binned) are grouped into bins of degree-width binWidth, max-degree localMax, and number of degrees in bin with at least one vertex binSize (to keep track of sparsity). This helps the authors to generate graphs with a more realistic degree distribution.
The process of generating a dynamic graph is as follows. First the difference between the desired and the current degree distribution is calculated. The authors then create an edge-addition set where each vertex is present as many times as the number of additional incident edges it must recieve. Edges are then created by connecting two vertices randomly from this set, and removing both from the set once connected. Currently, authors reject self-loops and duplicate edges. Removal of edges is done in a similar fashion.
Authors observe that adding edges with power-law properties dominates the execution time, and consider parallelizing DyGraph as part of future work.
Shared memory Parallelism (NOTES)
My notes on shared memory parallelism.
Shared memory is memory that may be simultaneously accessed by multiple programs with an intent to provide communication among them or avoid redundant copies. Shared memory is an efficient means of passing data between programs. Using memory for communication inside a single program, e.g. among its multiple threads, is also referred to as shared memory [REF].
A Dynamic Algorithm for Local Community Detection in Graphs : NOTES
**Community detection methods** can be *global* or *local*. **Global community detection methods** divide the entire graph into groups. Existing global algorithms include:
- Random walk methods
- Spectral partitioning
- Label propagation
- Greedy agglomerative and divisive algorithms
- Clique percolation
https://gist.github.com/wolfram77/b4316609265b5b9f88027bbc491f80b6
There is a growing body of work in *detecting overlapping communities*. **Seed set expansion** is a **local community detection method** where a relevant *seed vertices* of interest are picked and *expanded to form communities* surrounding them. The quality of each community is measured using a *fitness function*.
**Modularity** is a *fitness function* which compares the number of intra-community edges to the expected number in a random-null model. **Conductance** is another popular fitness score that measures the community cut or inter-community edges. Many *overlapping community detection* methods **use a modified ratio** of intra-community edges to all edges with atleast one endpoint in the community.
Andersen et al. use a **Spectral PageRank-Nibble method** which minimizes conductance and is formed by adding vertices in order of decreasing PageRank values. Andersen and Lang develop a **random walk approach** in which some vertices in the seed set may not be placed in the final community. Clauset gives a **greedy method** that *starts from a single vertex* and then iteratively adds neighboring vertices *maximizing the local modularity score*. Riedy et al. **expand multiple vertices** via maximizing modularity.
Several algorithms for **detecting global, overlapping communities** use a *greedy*, *agglomerative approach* and run *multiple separate seed set expansions*. Lancichinetti et al. run **greedy seed set expansions**, each with a *single seed vertex*. Overlapping communities are produced by a sequentially running expansions from a node not yet in a community. Lee et al. use **maximal cliques as seed sets**. Havemann et al. **greedily expand cliques**.
The authors of this paper discuss a dynamic approach for **community detection using seed set expansion**. Simply marking the neighbours of changed vertices is a **naive approach**, and has *severe shortcomings*. This is because *communities can split apart*. The simple updating method *may fail even when it outputs a valid community* in the graph.
Scalable Static and Dynamic Community Detection Using Grappolo : NOTES
A **community** (in a network) is a subset of nodes which are _strongly connected among themselves_, but _weakly connected to others_. Neither the number of output communities nor their size distribution is known a priori. Community detection methods can be divisive or agglomerative. **Divisive methods** use _betweeness centrality_ to **identify and remove bridges** between communities. **Agglomerative methods** greedily **merge two communities** that provide maximum gain in _modularity_. Newman and Girvan have introduced the **modularity metric**. The problem of community detection is then reduced to the problem of modularity maximization which is **NP-complete**. **Louvain method** is a variant of the _agglomerative strategy_, in that is a _multi-level heuristic_.
https://gist.github.com/wolfram77/917a1a4a429e89a0f2a1911cea56314d
In this paper, the authors discuss **four heuristics** for Community detection using the _Louvain algorithm_ implemented upon recently developed **Grappolo**, which is a parallel variant of the Louvain algorithm. They are:
- Vertex following and Minimum label
- Data caching
- Graph coloring
- Threshold scaling
With the **Vertex following** heuristic, the _input is preprocessed_ and all single-degree vertices are merged with their corresponding neighbours. This helps reduce the number of vertices considered in each iteration, and also help initial seeds of communities to be formed. With the **Minimum label heuristic**, when a vertex is making the decision to move to a community and multiple communities provided the same modularity gain, the community with the smallest id is chosen. This helps _minimize or prevent community swaps_. With the **Data caching** heuristic, community information is stored in a vector instead of a map, and is reused in each iteration, but with some additional cost. With the **Vertex ordering via Graph coloring** heuristic, _distance-k coloring_ of graphs is performed in order to group vertices into colors. Then, each set of vertices (by color) is processed _concurrently_, and synchronization is performed after that. This enables us to mimic the behaviour of the serial algorithm. Finally, with the **Threshold scaling** heuristic, _successively smaller values of modularity threshold_ are used as the algorithm progresses. This allows the algorithm to converge faster, and it has been observed a good modularity score as well.
From the results, it appears that _graph coloring_ and _threshold scaling_ heuristics do not always provide a speedup and this depends upon the nature of the graph. It would be interesting to compare the heuristics against baseline approaches. Future work can include _distributed memory implementations_, and _community detection on streaming graphs_.
Application Areas of Community Detection: A Review : NOTES
This is a short review of Community detection methods (on graphs), and their applications. A **community** is a subset of a network whose members are *highly connected*, but *loosely connected* to others outside their community. Different community detection methods *can return differing communities* these algorithms are **heuristic-based**. **Dynamic community detection** involves tracking the *evolution of community structure* over time.
https://gist.github.com/wolfram77/09e64d6ba3ef080db5558feb2d32fdc0
Communities can be of the following **types**:
- Disjoint
- Overlapping
- Hierarchical
- Local.
The following **static** community detection **methods** exist:
- Spectral-based
- Statistical inference
- Optimization
- Dynamics-based
The following **dynamic** community detection **methods** exist:
- Independent community detection and matching
- Dependent community detection (evolutionary)
- Simultaneous community detection on all snapshots
- Dynamic community detection on temporal networks
**Applications** of community detection include:
- Criminal identification
- Fraud detection
- Criminal activities detection
- Bot detection
- Dynamics of epidemic spreading (dynamic)
- Cancer/tumor detection
- Tissue/organ detection
- Evolution of influence (dynamic)
- Astroturfing
- Customer segmentation
- Recommendation systems
- Social network analysis (both)
- Network summarization
- Privary, group segmentation
- Link prediction (both)
- Community evolution prediction (dynamic, hot field)
<br>
<br>
## References
- [Application Areas of Community Detection: A Review : PAPER](https://ieeexplore.ieee.org/document/8625349)
Community Detection on the GPU : NOTES
This paper discusses a GPU implementation of the Louvain community detection algorithm. Louvain algorithm obtains hierachical communities as a dendrogram through modularity optimization. Given an undirected weighted graph, all vertices are first considered to be their own communities. In the first phase, each vertex greedily decides to move to the community of one of its neighbours which gives greatest increase in modularity. If moving to no neighbour's community leads to an increase in modularity, the vertex chooses to stay with its own community. This is done sequentially for all the vertices. If the total change in modularity is more than a certain threshold, this phase is repeated. Once this local moving phase is complete, all vertices have formed their first hierarchy of communities. The next phase is called the aggregation phase, where all the vertices belonging to a community are collapsed into a single super-vertex, such that edges between communities are represented as edges between respective super-vertices (edge weights are combined), and edges within each community are represented as self-loops in respective super-vertices (again, edge weights are combined). Together, the local moving and the aggregation phases constitute a stage. This super-vertex graph is then used as input fof the next stage. This process continues until the increase in modularity is below a certain threshold. As a result from each stage, we have a hierarchy of community memberships for each vertex as a dendrogram.
Approaches to perform the Louvain algorithm can be divided into coarse-grained and fine-grained. Coarse-grained approaches process a set of vertices in parallel, while fine-grained approaches process all vertices in parallel. A coarse-grained hybrid-GPU algorithm using multi GPUs has be implemented by Cheong et al. which grabbed my attention. In addition, their algorithm does not use hashing for the local moving phase, but instead sorts each neighbour list based on the community id of each vertex.
https://gist.github.com/wolfram77/7e72c9b8c18c18ab908ae76262099329
Survey for extra-child-process package : NOTES
Useful additions to inbuilt child_process module.
📦 Node.js, 📜 Files, 📰 Docs.
Please see attached PDF for literature survey.
https://gist.github.com/wolfram77/d936da570d7bf73f95d1513d4368573e
Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER
This paper presents two algorithms for efficiently computing PageRank on dynamically updating graphs in a batched manner: DynamicLevelwisePR and DynamicMonolithicPR. DynamicLevelwisePR processes vertices level-by-level based on strongly connected components and avoids recomputing converged vertices on the CPU. DynamicMonolithicPR uses a full power iteration approach on the GPU that partitions vertices by in-degree and skips unaffected vertices. Evaluation on real-world graphs shows the batched algorithms provide speedups of up to 4000x over single-edge updates and outperform other state-of-the-art dynamic PageRank algorithms.
Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...
For the PhD forum an abstract submission is required by 10th May, and poster by 15th May. The event is on 30th May.
https://gist.github.com/wolfram77/1c1f730d20b51e0d2c6d477fd3713024
Fast Incremental Community Detection on Dynamic Graphs : NOTES
In this paper, the authors describe two approaches for dynamic community detection using the CNM algorithm. CNM is a hierarchical, agglomerative algorithm that greedily maximizes modularity. They define two approaches: BasicDyn and FastDyn. BasicDyn backtracks merges of communities until each marked (changed) vertex is its own singleton community. FastDyn undoes a merge only if the quality of merge, as measured by the induced change in modularity, has significantly decreased compared to when the merge initially took place. FastDyn also allows more than two vertices to contract together if in the previous time step these vertices eventually ended up contracted in the same community. In the static case, merging several vertices together in one contraction phase could lead to deteriorating results. FastDyn is able to do this, however, because it uses information from the merges of the previous time step. Intuitively, merges that previously occurred are more likely to be acceptable later.
https://gist.github.com/wolfram77/1856b108334cc822cdddfdfa7334792a
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