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UID:149@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20250925T160000
DTEND;TZID=Asia/Kolkata:20250925T170000
DTSTAMP:20250918T122250Z
URL:https://cds.iisc.ac.in/events/mtech-research-thesis-defense-hybrid-cds
 -25-september-2025-scalable-distributed-frameworks-for-analysis-on-large-e
 volving-graphs/
SUMMARY:Mtech Research Thesis Defense: HYBRID: CDS: 25\, September 2025 "Sc
 alable Distributed Frameworks for analysis on Large Evolving Graphs"
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nMtech Research T
 hesis Defense\n\n\n\nSpeaker : Ms. Ruchi Bhoot\nS.R. Number : 06-18-01-10-
 22-22-1-20916\nTitle : "Scalable Distributed Frameworks for analysis on La
 rge Evolving Graphs"\nThesis examiner : Prof. Kishore Kothapalli\nResearch
  Supervisor : Prof. Yogesh Simmhan\nDate &amp\; Time : September 25\, 2025
  (Thursday) at 04:00 PM\nVenue : The Thesis Défense will be held on HYBRI
 D Mode\n# 102 CDS Seminar Hall /MICROSOFT TEAMS\n\nPlease click on the fol
 lowing link to join the Thesis Defense:\n\nMS Teams link\n\n\n\nABSTRACT\n
 \nThe analysis of graph-structured data has become increasingly important 
 as networks in various domains\, including science\, engineering\, and bus
 iness\, grow in size\, complexity\, and dynamism. While static graph analy
 sis has been well-explored\, there is now a shift towards understanding ho
 w networks evolve over time due to changes in vertices and edges. These ev
 olving networks are termed temporal graphs\, and they require new methods 
 to study their dynamic properties. However\, there is limited research on 
 distributed programming frameworks and scalable platforms for designing an
 d executing incremental algorithms on such time-varying graphs\, especiall
 y those updated at high rates in applications like social networks and fin
 ancial systems.\n\nIn response to the challenges of processing dynamic tem
 poral graphs\, we introduce TARIS\, a distributed platform designed to exe
 cute time-respecting algorithms on large-scale\, evolving networks. TARIS 
 extends the windowed Interval-centric Computing Model (ICM) by enabling it
  to perform incremental computation\, making it suitable for continuous gr
 aph updates. We formalize the properties of temporal algorithms and prove 
 that our model of incremental computing over streaming updates is equivale
 nt to recomputing from scratch. This helps reduce runtime by multiple orde
 rs of magnitude. TARIS uses optimized data structures to reduce memory acc
 ess and enhance locality during graph updates. We also propose scheduling 
 strategies to pipeline updates and computations\, and streaming strategies
  to adapt the execution window to variable input rates.\n\nRelated to this
  is the need to partitioning large evolving graphs whose edges stream in i
 ncrementally. Traditional partitioning methods prioritize balancing edge c
 ounts and minimizing vertex replication but overlook the community structu
 re inherent in many graphs. We leverage a novel optimization function that
  preserves local triangle counts and the graph's community structure and e
 valuate its scalable distributed runtime design. Our results report signif
 icantly improved triangle count metrics while maintaining edge balance and
  vertex replication efficiency and achieving scalability.\n\n\n\nALL ARE W
 ELCOME
CATEGORIES:Events,Thesis Defense
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