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UID:86@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20241212T100000
DTEND;TZID=Asia/Kolkata:20241212T110000
DTSTAMP:20241212T055809Z
URL:https://cds.iisc.ac.in/events/m-tech-research-thesis-colloquium-cds-sc
 alable-distributed-frameworks-for-analysis-on-large-evolving-graphs/
SUMMARY:Rescheduled: M.Tech Research Thesis {Colloquium}: CDS: "Scalable Di
 stributed Frameworks for analysis on Large Evolving Graphs"
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n\nM.Tech Researc
 h Thesis Colloquium\n\n\n\nSpeaker : Ms. Ruchi Bhoot\n\nS.R. Number : 06-1
 8-01-10-22-22-1-20916\n\nTitle : "Scalable Distributed Frameworks for anal
 ysis on Large Evolving Graphs"\n\nResearch Supervisor :Prof. Yogesh Simmha
 n\n\nDate &amp\; Time : December 11\, 2024 (Wednesday)\, 2:00pm\n\nVenue :
  # 102 CDS Seminar Hall\n\n\n\nABSTRACT\n\nThe analysis of graph-structure
 d data has become increasingly important as networks in various domains\, 
 including science\, engineering\, and business\, grow in size\, complexity
 \, and dynamism. While static graph analysis has been well-explored\, ther
 e is now a shift towards understanding how networks evolve over time due t
 o changes in vertices and edges. These evolving networks are termed tempor
 al graphs\, and they require new methods to study their dynamic properties
 . However\, there is limited research on distributed programming framework
 s and scalable platforms for designing and executing incremental algorithm
 s on such time-varying graphs\, especially those updated at high rates in 
 applications like social networks and financial systems.\n\nIn response to
  the challenges of processing dynamic temporal graphs\, we introduce TARIS
 \, a distributed platform designed to execute time-respecting algorithms o
 n large-scale\, evolving networks. TARIS extends the windowed Interval-cen
 tric Computing Model (ICM) by enabling it to perform incremental computati
 on\, making it suitable for continuous graph updates. We formalize the pro
 perties of temporal algorithms and prove that our model of incremental com
 puting over streaming updates is equivalent to recomputing from scratch. T
 his helps reduce runtime by multiple orders of magnitude. TARIS uses optim
 ized data structures to reduce memory access and enhance locality during g
 raph updates. We also propose scheduling strategies to pipeline updates an
 d computations\, and streaming strategies to adapt the execution window to
  variable input rates.\n\nRelated to this is the need to partitioning larg
 e evolving graphs whose edges stream in incrementally. Traditional partiti
 oning methods prioritize balancing edge counts and minimizing vertex repli
 cation but overlook the community structure inherent in many graphs. We le
 verage a novel optimization function that preserves local triangle counts 
 and the graph's community structure and evaluate its scalable distributed 
 runtime design. Our results report significantly improved triangle count m
 etrics while maintaining edge balance and vertex replication efficiency an
 d achieving scalability.\n\n\n\nALL ARE WELCOME
CATEGORIES:MTech Research Thesis Colloquium
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