BEGIN:VCALENDAR VERSION:2.0 X-WR-CALNAME:EventsCalendar PRODID:-//hacksw/handcal//NONSGML v1.0//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York LAST-MODIFIED:20240422T053451Z TZURL:https://www.tzurl.org/zoneinfo-outlook/America/New_York X-LIC-LOCATION:America/New_York BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:19700308T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:19701101T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT CATEGORIES:College of Engineering,Thesis/Dissertations DESCRIPTION:Advisor: Dr. Firas Khatib, Associate Professor, Department of C omputer and Information Science Committee Members: Dr. Debarun Das, Assist ant Professor, Department of Computer and Information ScienceDr. Ashokkuma r Patel, Associate Teaching Professor, Department of Computer and Informat ion Science Abstract: This thesis applies complex network science to a nalyse 35 years of global trade data and characterise the structural prope rties, temporal evolution, and resilience of the international trade syste m. Using 613,252 bilateral export observations from the UN Comtrade databa se covering 188 reporting economies from 1988 to 2022, the study construct s nine 4-year interval networks and one full-period aggregate network. Eac h country becomes a node, each bilateral trade relationship above a one mi llion USD threshold becomes a weighted edge, and the resulting graphs are analysed using small-world theory, scale-free network theory, geospatial c ommunity detection, and percolation-based resilience simulation.Nine forma l hypotheses are tested. Seven are confirmed, two are partially confirmed. The global trade network exhibits persistent small-world properties acros s all nine intervals, with small-world index σ consistently above 1.2, cl ustering coefficient in the range 0.82 to 0.86, and average path length be tween 1.38 and 1.75. The network densified substantially over the study pe riod, from 203 nodes and 3,694 edges in 1988 to 237 nodes and over 12,000 edges from 2008 onwards. The power-law exponent evolved from α = 2.38 in 1988 to α = 2.60 in 2020, indicating gradual structural shift toward less extreme hub dominance. Louvain community detection identifies three geogr aphically coherent trade blocs — Asia-Pacific, European, and North Ameri can — with a Pearson correlation of 0.72 between geographic proximity an d bilateral clustering confirming that geography drives trade network topo logy. Percolation analysis reveals asymmetric resilience. The network tole rates 60 to 75 per cent random node removal before fragmenting but collaps es at only 15 to 20 per cent targeted hub removal, with the largest connec ted component dropping from 98 per cent to 22 per cent. Cascade simulation under a severity 0.8 shock to China produces an 80 per cent immediate tra de loss and a 40-step recovery trajectory under active rerouting. These fi ndings identify the 15 to 20 per cent hub removal threshold as a critical structural vulnerability and provide empirical grounding for supply chain policies around regional diversification, strategic inventory, and backup hub strategies for semiconductors, energy, and pharmaceuticals. The thesis establishes the largest longitudinal trade network dataset yet analyzed a t this methodological depth, with all code and data publicly released for reproducibility For further information please contact Dr. Firas Khatib at fkhatib@umassd.edu. \nEvent page: https://www.umassd.edu/events/cms/s mall-world-spatial-network-analysis-of-global-supply-chains-using-internat ional-trade-data-19882022.php\nEvent link: https://us05web.zoom.us/j/86211 339649?pwd=36pCDAlu0IGRZsXUB1zGvGGox8mvEY.1 X-ALT-DESC;FMTTYPE=text/html:

Advisor:

Dr. Firas Khat ib\, Associate Professor\, Department of Computer and Information Science< /p>\n

Committee Members:

Dr. Debarun Das\, Assistant Professor\, Department of Computer and Information Science
Dr. Ashokkumar Patel\ , Associate Teaching Professor\, Department of Computer and Information Sc ience

Abstract:

This thesis applies complex networ k science to analyse 35 years of global trade data and characterise the st ructural properties\, temporal evolution\, and resilience of the internati onal trade system. Using 613\,252 bilateral export observations from the U N Comtrade database covering 188 reporting economies from 1988 to 2022\, t he study constructs nine 4-year interval networks and one full-period aggr egate network. Each country becomes a node\, each bilateral trade relation ship above a one million USD threshold becomes a weighted edge\, and the r esulting graphs are analysed using small-world theory\, scale-free network theory\, geospatial community detection\, and percolation-based resilienc e simulation.
Nine formal hypotheses are tested. Seven are confirmed\ , two are partially confirmed. The global trade network exhibits persisten t small-world properties across all nine intervals\, with small-world inde x σ consistently above 1.2\, clustering coefficient in the range 0.82 to 0.86\, and average path length between 1.38 and 1.75. The network densifie d substantially over the study period\, from 203 nodes and 3\,694 edges in 1988 to 237 nodes and over 12\,000 edges from 2008 onwards. The power-law exponent evolved from α = 2.38 in 1988 to α = 2.60 in 2020\, indicating gradual structural shift toward less extreme hub dominance. Louvain commu nity detection identifies three geographically coherent trade blocs — As ia-Pacific\, European\, and North American — with a Pearson correlation of 0.72 between geographic proximity and bilateral clustering confirming t hat geography drives trade network topology.

Percolation analysis reveals asymmetric resilience. The network tolerates 60 to 75 per cent ran dom node removal before fragmenting but collapses at only 15 to 20 per cen t targeted hub removal\, with the largest connected component dropping fro m 98 per cent to 22 per cent. Cascade simulation under a severity 0.8 shoc k to China produces an 80 per cent immediate trade loss and a 40-step reco very trajectory under active rerouting. These findings identify the 15 to 20 per cent hub removal threshold as a critical structural vulnerability a nd provide empirical grounding for supply chain policies around regional d iversification\, strategic inventory\, and backup hub strategies for semic onductors\, energy\, and pharmaceuticals. The thesis establishes the large st longitudinal trade network dataset yet analyzed at this methodological depth\, with all code and data publicly released for reproducibility

For further information please contact Dr. Firas Khatib at fkhatib@umas sd.edu.

Event page: https://www.umassd.edu/events/cms/sm all-world-spatial-network-analysis-of-global-supply-chains-using-internati onal-trade-data-19882022.php
Event link: https://us05web .zoom.us/j/86211339649?pwd=36pCDAlu0IGRZsXUB1zGvGGox8mvEY.1

DTSTAMP:20260430T162504 DTSTART;TZID=America/New_York:20260515T140000 DTEND;TZID=America/New_York:20260515T150000 LOCATION:Zoom: https://us05web.zoom.us/j/86211339649?pwd=36pCDAlu0IGRZsXUB1 zGvGGox8mvEY.1 SUMMARY;LANGUAGE=en-us:Small-World Spatial Network Analysis of Global Suppl y Chains Using International Trade Data (1988–2022) UID:1168ad3a04c741d6434ac4202042e5ba@www.umassd.edu END:VEVENT END:VCALENDAR