CSE 701: Fast Algorithms for Graph

Analytics

A. Erdem Sariyuce

Who am I?• My name is Erdem• Office: 323 Davis Hall• Office hours: Tuesday 10-12

• Research on graph (network) mining & management• Practical algorithms

• Streaming, distributed, parallel• Leverage the characteristics of real-world data• Fast graph analytics

Heard About Big-Data?• Yes, I do that• For graphs, mostly

• Not only big, but also• Dynamic

• Incomplete

• Noisy

• Distributed

Social Information

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Graphs are everywhere

What’s this class about?• Mining graphs to get hidden insights• By finding patterns in complex structure• New models and algorithms

• On large data that cannot be examined manually• Computationally challenging• Fast algorithms needed

• On dynamic, incomplete, noisy data

What’s this class about?• We will cover a range of topics about• Structure of real-world networks (graphs)

• Small-world

• Community structure

• …

• Practical algorithms for fast graph analytics• Centrality computation

• Community detection

• Graph partitioning

• …

You?• MS or PhD? What year?

• Any particular objective in this course?

• Any research interests?

Course Structure• Presentations

• Questions before class

• Discussion in class

• Literature survey (or one additional presentation)• If taking 3 credits

Presentations• Each week two papers• Paper list is available at the course website

• http://sariyuce.com/seminar.html

• Each paper will be presented by a single student• No groups

• Each student will present one paper (more on this later)

Before class• Questions on Piazza before each class (except presenter)• Everyone will read the papers!• I’ll post some guides on how to read a paper

• Questions are due Tuesday night 11.59pm• Open-ended• Thought-provoking• Unique• ‘What does Fig. 4 tell?’ is NOT a question

In class: Presentation• For each paper;• At least 1 hour presentation

• It will be highly interactive; by me and others

• You can find slides online, ask authors if needed• But don’t rely on those too much!

• Conference presentations are only for 15/20 mins

• Citation analysis for at least 10 mins

• References: Which papers are cited in this paper?

• Cited by: Which papers have cited this paper?• Google Scholar, Microsoft Academic Search

• You can get feedback on slides/talk before the class!• Ask timely

In class: Citation Analysis• References: Which papers are cited in this paper?• Briefly explain 5 references that form basis for the paper

• Cited by: Which papers have cited this paper?• Google scholar

• https://scholar.google.com/• Microsoft Academic search

• https://academic.microsoft.com/• Check the ones at top venues

• SIGKDD, WWW, WSDM, VLDB, SIGMOD, Nature, Science …

• Check the ones that got most citations• Microsoft Academic search has that

• Briefly explain 5 of those; what’s new there?

In Class: Discussion• Presenter will read the posted questions• From Piazza

• And initiate discussion• Give his/her opinion, others should chip in as well

• Class participation points will be earned here

• I might force you to be a volunteer :)

• Each class is 150 mins, so we have plenty of time

Grading is S/U• This is a seminar class! 75% is needed for an S

• 1 or 2 credits;• Paper presentation: 40%• Piazza questions: 30%• Class participation: 30%

• 3 credits;• Paper presentation: 40%• Piazza questions: 20%• Class participation: 20%• Literature survey (individual): 20%

• Or one additional presentation (we have 26 papers in total)• 16 students in total; 8 with 3 credits• Students can do an extra presentation to waive the literature survey

Literature Survey• On a particular subject• Find, read, and summarize/categorize the previous work

• Talk to me for the topic

• Report is required by the end of semester• Update on 6th or 7th week, will let you know

• If done well;• We can go for a paper!• Survey papers are cited most, can be quite impactful

Paper List and Schedule•We will decide on Piazza

• I’ll post it at the end of the class

•First Come First Serve, be quick!

Papers• Sep 11: Four Degrees of Separation

• Web Science Conference, 2012

• Cited by +500

• Small-world phenomena

• Analysis of the entire Facebook network!

• Sep 11: Graph structure in the Web• Computer Networks, 2000

• Cited by +3900

• Characterizes the web

• Bow-tie structure

Papers• Sep 18: Statistical properties of community structure in large

social and information networks• World Wide Web Conference (WWW), 2008• Cited by +860• Detailed analysis of real communities in a variety of domains• Interesting conclusions on community size

• Sep 18: Uncovering the overlapping community structure of complex networks in nature and society• Nature, 2005• Cited by +5000• Overlapping communities• Clique based formulation

Papers• Sep 25: Authoritative Sources in a Hyperlinked Environment

• Journal of ACM, 1999• Cited by +9400• Hubs and authorities• One of the most influential works

• Sep 25: Graphs over time: densification laws, shrinking diameters and possible explanations• SIGKDD, 2005• Cited by +2200• Best-paper in ’05, Test-of-time award in ‘16• First work on graph evolution

Papers• Oct 2: The Link Prediction Problem for Social Networks

• JASIST 2007• Cited by +9400• Hubs and authorities• One of the most influential works

• Oct 2: Simplicial closure and higher-order link prediction• PNAS 2018• Beyond pair-wise• Higher-order relationships• Adapting triangle closure

Papers• Oct 9: A Fast and High Quality Multilevel Scheme for Partitioning

Irregular Graphs• SIAM Journal on Scientific Computing (SISC), 1998• Cited by +4800, widely used• Ground-breaking work on graph partitioning• Efficient multi-level heuristics

• Oct 9: Experimental Analysis of Streaming Algorithms for Graph Partitioning• SIGMOD, 2019• Streaming graph partitioning• Comparative survey• Very important for production-level deployments

Papers• Oct 16: Incremental k-core decomposition: algorithms and

evaluation• Very Large Data Bases Journal (VLDBJ), 2016• Maintaining graph analytics for streaming graphs• k-core decomposition is a fundamental operation• Density pointers

• Oct 16: A Fast Order-Based Approach for Core Maintenance• ICDE, 2017• Better performance than the paper above• Additional indexing• Runtime vs. space trade-off

Papers• Oct 23: Maximizing the Spread of Influence through a Social

Network• SIGKDD 2003• Cited by +6300• Formalizing the viral marketing• Very influential

• Oct 23: Influence Maximization on Social Graphs: A Survey• TKDE, 2018• Comprehensive survey• All follow-ups since the paper above

Papers• Oct 30: Signed Networks in Social Media

• CHI 2010• Cited by +940• What happens if we have +/- labels on edges?• Structural balance theory verified

• Oct 30: A Survey of Signed Network Mining in Social Media• ACM CSUR 2016• Another comprehensive survey• Covers all graph mining works on signed networks

Papers• Nov 6: Finding a Maximum Density Subgraph

• Berkeley TR 1984• Beautiful theory paper• Finding a subgraph with largest average degree• Influenced many works

• Nov 6: Denser than the Densest Subgraph: Extracting Optimal Quasi-Cliques with Quality Guarantees• SIGKDD 2013• How to generalize the paper above for triangles?• Novel quasi-clique formulation

Papers• Nov 13: Trusses: Cohesive Subgraphs for Social Network Analysis

• NSA TR 2008• Generalization of k-core model to triangles• Influenced many works

• Nov 13: Finding the Hierarchy of Dense Subgraphs using Nucleus Decompositions• WWW 2015• Unification of core/truss models for higher orders• Hierarchical dense subgraph discovery

Papers• Nov 20: Network Motifs: Simple Building Blocks of Complex

Networks• Science, 2002

• Cited by +6100

• Small induced subgraphs

• Fundamental units of complex networks

• Nov 20: Uncovering Biological Network Function via Graphlet Degree Signatures• Cancer Informatics, 2008

• Cited by +270

• Extends degree concept to motifs

• Very simple local statistics to capture the node function

Papers• Nov 27: A Faster Algorithm for Betweenness Centrality

• Journal of Mathematical Sociology, 2001• Cited by +3100• Finding nodes that are central in the graph• Reduces complexity from O(V^3) to O(V.E)

• Nov 27: Centrality and network flow• Social Networks 2004 • Cited by +2700• Considers flows on edges• Not true that all paths are equally useful

Papers• Dec 4: Higher-order organization of complex networks

• Science, 2016• Cited by +150• Analyzing the higher-order structures (Not pair-wise relations)• How triangles and other small motifs impact the structure

• Dec 4: Representing higher-order dependencies in networks• Science Advances, 2016 • A different approach to model higher-order structures• Non-Markovian property