wolfram77

Adjusting PageRank parameters and Comparing results

While doing research work with [Prof. Kishore Kothapalli], and [Prof. Dip Sankar Banerjee].

Abstract — The effect of adjusting damping factor α and tolerance τ on iterations needed for PageRank computation is studied here. Relative performance of PageRank computation with L1, L2, and L∞ norms used as convergence check, are also compared with six possible mean ratios. It is observed that increasing the damping factor α linearly increases the iterations needed almost exponentially. On the other hand, decreasing the tolerance τ exponentially decreases the iterations needed almost exponentially. On average, PageRank with L∞ norm as convergence check is the fastest, quickly followed by L2 norm, and then L1 norm. For large graphs, above certain tolerance τ values, convergence can occur in a single iteration. On the contrary, below certain tolerance τ values, sensitivity issues can begin to appear, causing computation to halt at maximum iteration limit w

wolfram77

Highlighted notes on:
Variadic CRTP by Steve Dewhurst (28 March 2017)

Thus is a very good method of using CRTP for separation of concerns.

The Curiously‐Recurring Template Pattern (CRTP) is a pleasant and common C++ coding idiom. Basically it’s a way for a class to inherit a self‐customized capability.

wolfram77

Highlighted notes on:
Proceedings Scholar Metrics:
H Index of proceedings on Computer Science, Electrical &
Electronic Engineering, and Communications according to
Google Scholar Metrics (2009-2013)

The objective of this report is to present a list of proceedings (conferences, workshops, symposia, meetings) in the areas of Computer Science, Electrical & Electronic Engineering, and Communications covered by Google Scholar Metrics and ranked according to their h-index. Google Scholar Metrics only displays publications that have published at least 100 papers and have received at least one citation in the last five years (2009-2013). The searches were conducted between the 15th and 22nd of December, 2014. A total of 1208 proceedings have been identified.

wolfram77

Highlighted notes on:
Top CSE conferences list
(IIIT Hyderabad)

ICDE: International Conference on Data Engineering
WWW: International World Wide Web Conference
VLDB: International Conference on Very Large Databases
ICDM: International Conference on Data Mining
ICDE: International Conference on Data Engineering
WSDM: International Conference on Web Search and Data Mining\

wolfram77

Highlighted notes on:
Policy on stipend support for research students
(IIIT Hyderabad)

1st semester: 15000

Before comprehensive viva:
Base component: 9000
Performance component: ~9000

wolfram77

Report on:
Submitting the Thesis Evaluation Request by MS/PhD Students
(IIIT Hyderabad)

The thesis evaluation request should contain:

1. Essential details: name, roll no, advisors, key contributions
2. Synopsis: background, key references (2-3 pages)

wolfram77

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.

wolfram77

Simplifying expression for delta modularity (Louvain algorithm, v2).
For details, see here: https://www.youtube.com/watch?v=0zuiLBOIcsw

The Louvain method for community detection is a method to extract communities from large networks created by Blondel et al.[1] from the University of Louvain (the source of this method's name). The method is a greedy optimization method that appears to run in time O(n.log n) if n is the number of nodes in the network.[2]

wolfram77

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 detect

wolfram77

Highlighted notes on Optimizing Parallel Reduction in CUDA
While doing research work under Prof. Dip Banerjee, Prof. Kishore Kothapalli.

Interesting optimizations, i should try these soon as PageRank is basically lots of sums.