We show a new relationship between various community detection objectives and a correlation clustering framework. These enable us to detect communities with good bounds on the solution.
UMAP is a technique for dimensionality reduction that was proposed 2 years ago that quickly gained widespread usage for dimensionality reduction.
In this presentation I will try to demistyfy UMAP by comparing it to tSNE. I also sketch its theoretical background in topology and fuzzy sets.
Quick introduction to community detection.
Structural properties of real world networks, definition of "communities", fundamental techniques and evaluation measures.
UMAP is a technique for dimensionality reduction that was proposed 2 years ago that quickly gained widespread usage for dimensionality reduction.
In this presentation I will try to demistyfy UMAP by comparing it to tSNE. I also sketch its theoretical background in topology and fuzzy sets.
Quick introduction to community detection.
Structural properties of real world networks, definition of "communities", fundamental techniques and evaluation measures.
Being an Iranian Woman Today イラン人女性として現代に生きるということParisa Mehran
In this presentation, I will talk about the challenges and opportunities of being an Iranian woman today. I will challenge existing stereotypes and misunderstandings about Iranian women by sharing personal narratives and by recounting stories of Iranian women in power and leadership.
この発表では、イラン人女性として現代を生きる上での困難と機会についてお話します。イラン人女性に対しては様々な偏見や誤解がありますが、今日は私の個人的な経験や、リーダーシップを持って力強く生きているイラン人女性たちのことをお話することで、ステレオタイプを打ち破っていきたいと思います。
Higher-order organization of complex networksDavid Gleich
A talk I gave at the Park City Institute of Mathematics about our recent work on using motifs to analyze and cluster networks. This involves a higher-order cheeger inequality in terms of motifs.
Anti-differentiating approximation algorithms: A case study with min-cuts, sp...David Gleich
This talk covers the idea of anti-differentiating approximation algorithms, which is an idea to explain the success of widely used heuristic procedures. Formally, this involves finding an optimization problem solved exactly by an approximation algorithm or heuristic.
Localized methods for diffusions in large graphsDavid Gleich
I describe a few ongoing research projects on diffusions in large graphs and how we can create efficient matrix computations in order to determine them efficiently.
Overlapping community detection in Large-Scale Networks using BigCLAM model b...Thang Nguyen
In this undergraduate thesis, I provide a general view of communities and its the real life applications. In recent years, with the rapid growth of network scale, it is a difficult task to detect overlapping communities in large-scale networks for state of the art methods. This method is implemented in the Apache Spark framework for its power in distributed parallel computation.
The main contributions of this work include:
Introduce BigCLAM models proposed by Yang and Leskovec (2013).
proposed a few methods convex optimization.
implemented BigCLAM in Apache Spark is evaluated as lightning-fast cluster computing to able detect community in the large-scale networks.
https://thangdnsf.github.io/research.html
Big data matrix factorizations and Overlapping community detection in graphsDavid Gleich
In a talk at the Chinese Academic of Sciences Institute for Automation, I discuss some of the MapReduce and community detection methods I've worked on.
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Being an Iranian Woman Today イラン人女性として現代に生きるということParisa Mehran
In this presentation, I will talk about the challenges and opportunities of being an Iranian woman today. I will challenge existing stereotypes and misunderstandings about Iranian women by sharing personal narratives and by recounting stories of Iranian women in power and leadership.
この発表では、イラン人女性として現代を生きる上での困難と機会についてお話します。イラン人女性に対しては様々な偏見や誤解がありますが、今日は私の個人的な経験や、リーダーシップを持って力強く生きているイラン人女性たちのことをお話することで、ステレオタイプを打ち破っていきたいと思います。
Higher-order organization of complex networksDavid Gleich
A talk I gave at the Park City Institute of Mathematics about our recent work on using motifs to analyze and cluster networks. This involves a higher-order cheeger inequality in terms of motifs.
Anti-differentiating approximation algorithms: A case study with min-cuts, sp...David Gleich
This talk covers the idea of anti-differentiating approximation algorithms, which is an idea to explain the success of widely used heuristic procedures. Formally, this involves finding an optimization problem solved exactly by an approximation algorithm or heuristic.
Localized methods for diffusions in large graphsDavid Gleich
I describe a few ongoing research projects on diffusions in large graphs and how we can create efficient matrix computations in order to determine them efficiently.
Overlapping community detection in Large-Scale Networks using BigCLAM model b...Thang Nguyen
In this undergraduate thesis, I provide a general view of communities and its the real life applications. In recent years, with the rapid growth of network scale, it is a difficult task to detect overlapping communities in large-scale networks for state of the art methods. This method is implemented in the Apache Spark framework for its power in distributed parallel computation.
The main contributions of this work include:
Introduce BigCLAM models proposed by Yang and Leskovec (2013).
proposed a few methods convex optimization.
implemented BigCLAM in Apache Spark is evaluated as lightning-fast cluster computing to able detect community in the large-scale networks.
https://thangdnsf.github.io/research.html
Big data matrix factorizations and Overlapping community detection in graphsDavid Gleich
In a talk at the Chinese Academic of Sciences Institute for Automation, I discuss some of the MapReduce and community detection methods I've worked on.
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
N-gram IDF: A Global Term Weighting Scheme Based on Information Distance (WWW...Masumi Shirakawa
A deck of slides for "N-gram IDF: A Global Term Weighting Scheme Based on Information Distance" (Shirakawa et al.) that was presented at 24th International World Wide Web Conference (WWW 2015).
Spectral clustering with motifs and higher-order structuresDavid Gleich
I presented these slides at the #strathna meeting in Glasgow in June 2017. They are an updated and enhanced version of the earlier talks on the subject.
Spacey random walks and higher-order data analysisDavid Gleich
My talk at TMA 2016 (The workshop on Tensors, Matrices, and their Applications) on the relationship between a spacey random walk process and tensor eigenvectors
A copy of my slides from the SILO Seminar at UW Madison on our recent developments for the NEO-K-Means methods including new optimization routines and results.
Using Local Spectral Methods to Robustify Graph-Based LearningDavid Gleich
This is my KDD2015 talk on robustness in semi-supervised learning. The paper is already on Michael Mahoney's website: http://www.stat.berkeley.edu/~mmahoney/pubs/robustifying-kdd15.pdf See the KDD paper for all the details, which this talk is a bit light on.
Spacey random walks and higher order Markov chainsDavid Gleich
My talk at SIAM NetSci workshop (2015) on our new spacey random walk and spacey random surfer models and how we derived them. There many potential extensions and opportunities to use this for analyzing big data as tensors.
Localized methods in graph mining exploit the local structures in a graph instead attempting to find global structures. These are widely successful at all sorts of problems including community detection, label propagation, and a few others.
PageRank Centrality of dynamic graph structuresDavid Gleich
A talk I gave at the SIAM Annual Meeting Mini-symposium on the mathematics of the power grid organized by Mahantesh Halappanavar. I discuss a few ideas on how our dynamic centrality could help analyze such situations.
Anti-differentiating Approximation Algorithms: PageRank and MinCutDavid Gleich
We study how Google's PageRank method relates to mincut and a particular type of electrical flow in a network. We also explain the details of how the "push method" for computing PageRank helps to accelerate it. This has implications for semi-supervised learning and machine learning, as well as social network analysis.
Fast relaxation methods for the matrix exponential David Gleich
The matrix exponential is a matrix computing primitive used in link prediction and community detection. We describe a fast method to compute it using relaxation on a large linear system of equations. This enables us to compute a column of the matrix exponential is sublinear time, or under a second on a standard desktop computer.
Fast matrix primitives for ranking, link-prediction and moreDavid Gleich
I gave this talk at Netflix about some of the recent work I've been doing on fast matrix primitives for link prediction and also some non-standard uses of the nuclear norm for ranking.
Gaps between the theory and practice of large-scale matrix-based network comp...David Gleich
I discuss some runtimes for the personalized PageRank vector and how it relates to open questions in how we should tackle these network based measures via matrix computations.
MapReduce Tall-and-skinny QR and applicationsDavid Gleich
A talk at the SIMONS workshop on Parallel and Distributed Algorithms for Inference and Optimization on how to do tall-and-skinny QR factorizations on MapReduce using a communication avoiding algorithm.
Recommendation and graph algorithms in Hadoop and SQLDavid Gleich
A talk I gave at ancestry.com on Hadoop, SQL, recommendation and graph algorithms. It's a tutorial overview, there are better algorithms than those I describe, but these are a simple starting point.
Relaxation methods for the matrix exponential on large networksDavid Gleich
My talk from the Stanford ICME seminar series on doing network analysis and link prediction using the a fast algorithm for the matrix exponential on graph problems.
This talk is a new update based on some of our recent results on doing Tall and Skinny QRs in MapReduce. In particular, the "fast" iterative refinement approximation based on a sample is new.
Vertex neighborhoods, low conductance cuts, and good seeds for local communit...David Gleich
My talk from KDD2012 about vertex neighborhoods and low conductance cuts. See the paper here: http://arxiv.org/abs/1112.0031 and http://dl.acm.org/citation.cfm?id=2339628
How does Google Google: A journey into the wondrous mathematics behind your f...David Gleich
A talk I gave at the annual meeting for the MetroNY section of the MAA about how Google works from a link-ranking perspective. (http://sections.maa.org/metrony/)
Based on a talk by Margot Gerritsen (which used elements from another talk I gave years ago, yay co-author improvements!)
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
Correlation clustering and community detection in graphs and networks
1. New relationships and algorithms
for correlation clustering and
community detection
David F. Gleich
Purdue University
With Nate Veldt (Purdue), Tony Wirth (Melbourne),
and James Saunderson (Monash)
Paper arXiv:1806.01678, 1712.05825 Code github.com/nveldt/LamCC, github.com/nveldt/MetricOptimization
UIUC 1David Gleich · Purdue
2. Graph clustering seeks“communities”of nodes in a network
Objective
functions
All seek to
balance
High internal densityLow external connectivity
modularity, densest subgraph, maximum
clique, conductance, sparsest cut, etc.
David Gleich · Purdue 2UIUC
3. Two objectives at opposite ends of the spectrum
min
cut(S)
`S`
+
cut(S)
`¯S`
Sparsest cut
David Gleich · Purdue 3UIUC
4. Sparsest cut
Minimize number of edges removed
to partition graph into cliques
Two objectives at opposite ends of the spectrum
Cluster Deletion
min
cut(S)
`S`
+
cut(S)
`¯S`
David Gleich · Purdue 4UIUC
5. We show sparsest cut and cluster deletion are two special
cases of the same new clustering framework:
LAMBDACC = λ Correlation Clustering
This framework also leads to
- new connections to other objectives (including modularity!)
- new approximation algorithms (2-approx for cluster deletion)
- several experiments/applications (social network analysis)
- (aside) fast method for LPs w/ metric constraints (for approx. algs)
David Gleich · Purdue 5UIUC
6. 6
Our framework is
based on correlation
clustering
Edges in a signed
graph indicate
similarity (+)
or dissimilarity (-)
UIUCDavid Gleich · Purdue
7. i
j
k
Edges can be weighted, but problems
become harder.
w+
ij wjk
w+
ij wjk
7
Our framework is
based on correlation
clustering
Edges in a signed
graph indicate
similarity (+)
or dissimilarity (-)
UIUCDavid Gleich · Purdue
8. Our framework is
based on correlation
clustering
Edges in a signed
graph indicate
similarity (+)
or dissimilarity (-)
i
j
k
Mistake Mistake
Objective: Minimize the weight of “mistakes”
w+
ij wjk
w+
ij wjk
8UIUCDavid Gleich · Purdue
9. Given G = (V,E), construct signed
graph G’ = (V,E+,E- ), an instance
of correlation clustering
You can use correlation clustering to cluster unsigned graphs
David Gleich · Purdue 9
+
++
–
–
–
+
+ –
To model sparsest cut or cluster
deletion, set resolution parameter
λ ∈ (0,1)
LAMBDACC
1
1
1
1
1
Without weights, unweighted
correlation clustering is the same
as cluster editing
UIUC
10. Consider a restriction to two clusters
Positive mistakes: (1 – λ) cut(S)
Negative mistakes: λ |E–| – λ [ |S| |S| – cut(S) ]
Total weight of mistakes =
David Gleich · Purdue 10
S S
cut(S)– λ |S| |S| + λ |E–|
UIUC
11. This is a scaled version of sparsest cut!
minimize cut(S) `S``¯S` + `E `
constantTwo-cluster LAMBDACC can be written
cut(S) `S``S` < 0 ()
cut(S)
`S``S`
<Note
David Gleich · Purdue 11
cut(S)
`S`
+
cut(S)
`S`
= `V`
cut(S)
`S``S`
UIUC
12. We can write the objective in terms of cuts to get a
relationship with sparsest cut.
The general LAMBDACC objective can be written
THEOREM
Minimizing this objective produces clusters with scaled sparsest
cut at most λ (if they exist). There exists some λ’ such that
minimizing LAMBDACC will return the minimum sparsest cut
partition.
minimize
1
2
kX
i=1
cut(Si)
2
kX
i=1
`Si``Si` + `E `
David Gleich · Purdue 12UIUC
13. We show this is
equivalent to LAMBDACC
for the right choice of
λ ≫ (1-λ)
1
1
1
1
1
cluster deletion correlation clustering with infinite
penalties on negative edges
David Gleich · Purdue 13
1
1
1
1
1
For large λ,LAMBDACC generalizes cluster deletion
UIUC
14. 1 2 1 4
3
2
4
1 2 3 4
1 2 3 4
1 2 3 4
1 2 3 4
1 2 3 4
1 6
1 6
4
2
1 6
Degree-weighted LAMBDACC is related to Modularity
Though this does not preserve approximations…
LAMBDACC is a linear function of Modularity
Positive weight: 1 – λdidj
Negative weight: λdidj
David Gleich · Purdue 14UIUC
16. Algorithms for LAMBDACC are closely related to other
algorithms for correlation clustering,with better bounds
Any weighted correlation clustering objective gives a O(log n)
approximation via LP relax-and-round approaches.
Adapting the approach of van Zuylen and Williamson we obtain new
algorithms for standard LambdaCC based on the same LP relaxations:
• ThreeLP: 3-approximation for LAMBDACC when λ > ½
• TwoLP: 2-approximation for cluster deletion
We also provide scalable heuristic algorithms
• Lambda-Louvain: based on Louvain method for modularity
• GrowCluster: greedy agglomeration technique
16
[A.van Zuylen and D.P.Williamson.Mathematics of
Operations Research,34(3):594–620,2009.]
Best known
approximation for
cluster deletion!
UIUCDavid Gleich · Purdue
We get a 5-approx. via
Puleo & Milenkovic
2015 when λ > 1/2
17. The ThreeLP algorithms begins by solving a metric-
constrained LP-relaxation and then rounding it.
In a few minutes, we’ll talk about how to solve metric LPs using some
scalable approaches.
ALG. Solve (2).
Create a graph F where Fij = +1 if Xij >= 1/3, Fij = -1 if Xij < 1/3.
Run Pivot on F.
UIUCDavid Gleich · Purdue 17
minimize
P
ij2E+ (1 )Xij
+
P
ij2E (1 Xij)
subject to Xij Xik + Xjk
for all i, j, k
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BLP for min-disagree Relaxed MetricLP (2)
18. Our heuristic algorithms work in a greedy fashion.
GrowCluster.
• Locally produce a cluster by greedily starting from a random seed and
growing out to the best vertex in the boundary.
• Add the best cluster found, remove those vertices from the graph, and
then repeat until the graph is empty
LambdaLouvain.
• Use the generalized Louvain procedure of Jeub, Bazzi, Jutla, Porter
tweaked to optimize the LambdaCC objective.
• (This merges vertices into a cluster greedily starting from singletons)
UIUCDavid Gleich · Purdue 18
19. n*Lambda
0 0.05 0.1 0.15 0.2 0.25
ProbabilityofSharedAttribute
0
0.2
0.4
0.6
0.8
1
24 101 168 247 312 399 485
S/F
Dorm
Year
Cornell University (Facebook100)
David Gleich · Purdue 19
We cluster social networks with various λ to understand the
correlation between communities and metadata attributes
Student/faculty status
Dorm
Graduation year
UIUC
20. n*Lambda
0 0.05 0.1 0.15 0.2 0.25
ProbabilityofSharedAttribute
0
0.2
0.4
0.6
0.8
1
24 101 168 247 312 399 485
S/F
Dorm
Year
Probability that two people who share a
cluster also share a metadata attribute
Cornell University (Facebook100)
David Gleich · Purdue 20
We cluster social networks with various λ to understand the
correlation between communities and metadata attributes
Student/faculty status
Dorm
Graduation year
UIUC
21. n*Lambda
0 0.05 0.1 0.15 0.2 0.25
ProbabilityofSharedAttribute
0
0.2
0.4
0.6
0.8
1
24 101 168 247 312 399 485
S/F
Dorm
Year
Probability that two people who share a
cluster also share a metadata attribute
Cornell University (Facebook100)
David Gleich · Purdue 21
Probability that they share a related
fake attribute
Student/faculty status
Dorm
Graduation year
We cluster social networks with various λ to understand the
correlation between communities and metadata attributes
UIUC
22. n*Lambda
0 0.05 0.1 0.15 0.2 0.25
ProbabilityofSharedAttribute
0
0.2
0.4
0.6
0.8
1
24 101 168 247 312 399 485
S/F
Dorm
Year
Probability that two people who share a
cluster also share a metadata attribute
Cornell University (Facebook100)
The gap shows that there is a
noticeable correlation between
each attribute and the clustering
David Gleich · Purdue 22
Probability that they share a related
fake attribute
Student/faculty status
Dorm
Graduation year
We cluster social networks with various λ to understand the
correlation between communities and metadata attributes
UIUC
24. n*Lambda
0 0.05 0.1 0.15 0.2 0.25
ProbabilityofSharedAttribute
0
0.2
0.4
0.6
0.8
1
17 71 131 225 363 528 711
S/F
Dorm
Year
n*Lambda
0 0.05 0.1 0.15 0.2 0.25
ProbabilityofSharedAttribute
0
0.2
0.4
0.6
0.8
1
4 47 136 242 351 452 533
S/F
Dorm
Year
S/F status and graduation year peak early
Swarthmore
Yale
David Gleich · Purdue 24UIUC
25. n*Lambda
0 0.05 0.1 0.15 0.2 0.25
ProbabilityofSharedAttribute
0
0.2
0.4
0.6
0.8
1
17 71 131 225 363 528 711
S/F
Dorm
Year
n*Lambda
0 0.05 0.1 0.15 0.2 0.25
ProbabilityofSharedAttribute
0
0.2
0.4
0.6
0.8
1
4 47 136 242 351 452 533
S/F
Dorm
Year
S/F status and graduation year peak early
Dorm attribute is more
correlated with small,
dense communities
Swarthmore
Yale
David Gleich · Purdue 25UIUC
26. And now, an answer to one of the
most frequently asked questions in
clustering.
“What method should I use”?
UIUCDavid Gleich · Purdue 26
27. Changing your method (implicitly) changes the value of
λ that you are using.
Lambda
1e-05 0.00022 0.0046 0.1 0.25 0.55 0.85
RatiotoLPbound
1
2
3
4
Graclus
Louvain
InfoMap
RMQC
RMC
Dense subgraph regimeSparse cut regime
This figure shows that if you
use one of these algorithms
(Graclus, Louvain, InfoMap,
recursive max-quasi clique,
or recursive max-clique)
then you implicitly
minimize λ-CC for some
choice of λ.
Turns the question
“what method should I use?”into
“what λ should I use?”
UIUC 27David Gleich · Purdue
28. Changing your method (implicitly) changes the value of
λ that you are using.
Lambda
1e-05 0.00022 0.0046 0.1 0.25 0.55 0.85
RatiotoLPbound
1
2
3
4
Graclus
Louvain
InfoMap
RMQC
RMC
Dense subgraph regimeSparse cut regime
This figure shows that if you
use one of these algorithms
(Graclus, Louvain, InfoMap,
recursive max-quasi clique,
or recursive max-clique)
then you implicitly
minimize λ-CC for some
choice of λ.
Turns the question
“what method should I use?”into
“what λ should I use?”
UIUC 28David Gleich · Purdue
The rest of the talk is an aside on
how we made this figure!
LP bound involves an LP
with 12 billion constraints.
29. For the rest of the talk, we’ll discuss
solving LPs with up to 700 billion
metric constraints.
UIUCDavid Gleich · Purdue 29
30. Metric constrained LPs show up in a variety of
approximation algorithms for NP-hard problems.
A distance metric X is a matrix that encodes distances between n points.
Paradigm. Formulate NP-hard problem as optimization over {0,1}-metrics.
Then relax to [0,1]-metrics to get a linear program and bound.
Examples. Leighton-Rao sparsest cut, correlation clustering, cluster editing,
modularity optimization, cluster deletion.
Challenge. These LPs are really hard to solve as they have n3 constraints.
· Xij = distance from i to j
· Xii = 0, Xij 0
· Xij = Xji
· Xik Xij + Xik<latexit sha1_base64="02GYV6F8DvHZ7Ao+7wxUq3E60Uk=">AAAFtHicbVTtbhM5FJ3ABtjswhb4B3/cbYtWS6gmiF26P5AqLaooAm0hKVSqo67HcydxY88MtocmWH4rXmbfYB+D63zQTBpLyVxdn3OP7/FHUkphbBz/17h2/YfmjZu3fmz99PPtO79s3L33wRSV5nDMC1nok4QZkCKHYyushJNSA1OJhI/J6O8w//EzaCOKvGcnJfQVG+QiE5xZTJ1tfKU8LSyhnyqWkkfk5MyJc09eEGphbB1JcQUs50AyXSiyLbaJLcj2+bYnlJLWVa4I3Li9qEMHQGKErkFOVTA4F349YIR5CQvs4+852mqdbWzFu/F0kKtBZx5sRfNxdHb3+v80LXilILdcMmNOO3Fp+45pK7gE36KVgZLxERvAKYY5U2D6bmqvJzuYSUlWaPzllkyzrWUK1tFsUqviLEsqyfS4nk2KYoQzxrfqkjbb6zuRl5WFnM8Us0oGs8Om4TZo4FZOSF3Wjr48GWhWDmciVoy+SJFopidhRcWFaZshK8G0OZO8nQmLuOnqJVjXqzIL7yH1TkO6uRdvJhLLLiPsEAYaIPdu+gmYi6GwsIJJZAXehf8lRL2/XqfvgnehuVoHR70uy9ENqiGHC14oxfLU0YwpIScpZKyS1jtqskVcN8BkilnsfmdZzGC3kL6Id/9qcyVQFC2SuJsoYMcmCyUUGwHDu2It6BbF2jS341Bqf0Z25vdTPEZ/9P0CW2CjYdNfAp4fDd2JSgp5gC25WRXj3T9v33iXBwklvFPeTf3ugl0HxkS6SknmlLlGIHSrxOCtrsJlXS+wqtA9eBssWQj0OjX7XDL2zshLkQCesd0hIoMHTJZD5i+X+u/hiuvpQILgwycz79fN4EYbvDn1o69CmeVdVl0xUKhEEV1pCOUcTZSjs7y/cizUG3zY0nWM+QRS8G3orL4EV4MPT3c78W7n3bOt/b35K3Erehj9Gv0WdaLn0X70KjqKjiPeeNDYbxw2Xjf/bNImb8IMeq0x59yPaqOZfwPXG/wm</latexit><latexit sha1_base64="02GYV6F8DvHZ7Ao+7wxUq3E60Uk=">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</latexit><latexit sha1_base64="02GYV6F8DvHZ7Ao+7wxUq3E60Uk=">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</latexit><latexit sha1_base64="02GYV6F8DvHZ7Ao+7wxUq3E60Uk=">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</latexit>
Note. Some consider our definition to
be a pseudo- or semi-metric because
we allow Xij = 0 for i ≠ j
Xik ≤ Xij + Xik is a triangle constraintLinear
constraints!
UIUC 30David Gleich · Purdue
31. The Leighton-Rao problem for sparsest cut.
Sparsest cut. Find a set S that has the smallest boundary-to-size ratio for
itself and its complement.
The metric problem. Encode the set S as a distance metric X where Xij = 0 if
i,j are in S or its complement and Xij = const. if the edge is cut.
The LP.
S S
minimize cut(S)/|S| + cut(¯S)/|¯S|<latexit sha1_base64="6ajYtNpiJP2CFKiADuZROdE/BgY=">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</latexit><latexit sha1_base64="6ajYtNpiJP2CFKiADuZROdE/BgY=">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</latexit><latexit sha1_base64="6ajYtNpiJP2CFKiADuZROdE/BgY=">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</latexit><latexit sha1_base64="6ajYtNpiJP2CFKiADuZROdE/BgY=">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</latexit>
, minimize cut(S)/(|S||¯S|)<latexit sha1_base64="p+UA9CoDFlmfmJF5Walt0YoDbBo=">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</latexit><latexit sha1_base64="p+UA9CoDFlmfmJF5Walt0YoDbBo=">AAAFgHicbVThb9w0FM/KDsYxWMc+8iWjTOpQ1yUIREEMVWKaNqnTCrluk+pTsZ2XO+vsJLNf6F1d/0P8NXwdfw12krLmWktJXp5/7/38fn42q6UwmCTvb2x8dHP08Se3Ph1/dvvzL+5s3v3ytakazeGIV7LSbxk1IEUJRyhQwttaA1VMwhu2+C3Mv/kLtBFVOcFVDVNFZ6UoBKfoXSebT8kBFKjFbI5U6+o0Ju8amsdEiVIocQb9vyUIS7S8QbedPXy8fZ6dnxNGtc3c+UN3srmV7CbtiK8aaW9sRf04PLl78xuSV7xRUCKX1JjjNKlxaqlGwSW4MWkM1JQv6AyOvVlSBWZq23Jd/MB78riotH9KjFvv+HKIz6PpapDFImWNpHo59LKqWvgZ48ZDSiz2plaUdYNQ8o6xaGSMVRxEjHOhgaNcxUNaXJw9mmlazzsSFIszKZimemVbcc2OmdMazA6nku8UAj2uXb0EtJOmQPgDcmc15Pf3kvtM+rSXETiHmQYonW0/AXM6FwhrGCYbcDa8LyGG9U3SqQ3aheIGFRxOMlp6NYiGEk55pRQtc0sKqoRc5VDQRqKzxBQX9lAAUyiKvvoHl8mMrxbyJ8nuTzvctxX6Iqj0u+kJcGmKkKLlko0qg7r2ubO/eg5AVi2TJ2TuP4TNdNXUjv9iCXTmvnVtWYougPq+RwQ9Jn5dpMRlWMZ+R2zNt8e+BX+Yugts5UUKDfMUfO9pyFaKVfKZl8N2WYyzr14eOFuG5SnhrHK23asM8Dqwd+TrIawP6TlCQNYw409oEw7e9QTrDNmzl0HOC4JJOpDesqWzRn4gCeAu2r7wyKABlfWcug9L/fPF2o7lMwmCzx91+3bdjG8S40/d8Nio/3et7xCViZnyTMSjGw0hnSVMWdL53ZWWUgf+ksqvi+gnfIi/V9L1W+Sq8fq73TTZTX//fmt/r79hbkVfRV9H21Ea/RjtR8+jw+go4tHf0T/R++jf0cZoe/R4lHbQjRt9zL1oMEY//wfqN/Ns</latexit><latexit sha1_base64="p+UA9CoDFlmfmJF5Walt0YoDbBo=">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</latexit><latexit sha1_base64="p+UA9CoDFlmfmJF5Walt0YoDbBo=">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</latexit>
minimize
X
P
ij AijXij
subject to
P
ij Xij = n, Xij 0
Xij Xik + Xjk for all i, j, k<latexit sha1_base64="Jgpj4JELAcDXxwqa2wJZWf20v0M=">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</latexit><latexit sha1_base64="Jgpj4JELAcDXxwqa2wJZWf20v0M=">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</latexit><latexit sha1_base64="Jgpj4JELAcDXxwqa2wJZWf20v0M=">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</latexit><latexit sha1_base64="Jgpj4JELAcDXxwqa2wJZWf20v0M=">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</latexit>
Also. This is related to multi-
commodity flow.
UIUC 31David Gleich · Purdue
Aij is the adjacency matrix
32. How well does Gurobi do at solving this LP?
Graph |V| |E| # constraints Gurobi Time
Jazz 198 2742 3.8 ⇥ 106
60
SmallW 233 994 6.2 ⇥ 106
93
C.El-Neural 297 2148 1.2 ⇥ 107
274
USAir97 332 2126 1.8 ⇥ 107
471
Netscience 379 914 2.7 ⇥ 107
887
Erdos991 446 1413 4.4 ⇥ 107
2574
C.El-Meta 453 2025 4.6 ⇥ 107
2497
Harvard500 500 2043 6.2 ⇥ 107
18769
Roget 994 3640 4.9 ⇥ 108
out of memory
SmaGri 1024 4916 5.4 ⇥ 108
out of memory
Email 1133 5451 7.3 ⇥ 108
out of memory
Polblogs 1222 16714 9.1 ⇥ 108
out of memory
Vassar85 3068 119161 1.4 ⇥ 1010
out of memory
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We setup Gurobi to skip the
cross-over step and just use
the minimum time of
SIMPLEX vs. Interior-point.
• Interior point always wins
We limited experiments to
100GB of RAM for
reproducibility. Gurobi used
all 28-cores on our system.
UIUC 32David Gleich · Purdue
33. Our DykstraSC solver provides an extra order-of-
magnitude in problem solvablility.
Graph |V| |E| # constraints Gurobi Time Dykstra Time Approx
Jazz 198 2742 3.8 ⇥ 106
60 81 1.003
SmallW 233 994 6.2 ⇥ 106
93 166 1.001
C.El-Neural 297 2148 1.2 ⇥ 107
274 350 1.000
USAir97 332 2126 1.8 ⇥ 107
471 511 1.041
Netscience 379 914 2.7 ⇥ 107
887 1134 1.000
Erdos991 446 1413 4.4 ⇥ 107
2574 1954 1.011
C.El-Meta 453 2025 4.6 ⇥ 107
2497 1138 1.000
Harvard500 500 2043 6.2 ⇥ 107
18769 1427 1.000
Roget 994 3640 4.9 ⇥ 108
out of memory 53449 1.008
SmaGri 1024 4916 5.4 ⇥ 108
out of memory 25703 1.002
Email 1133 5451 7.3 ⇥ 108
out of memory 34621 1.005
Polblogs 1222 16714 9.1 ⇥ 108
out of memory 41080 1.013
Vassar85 3068 119161 1.4 ⇥ 1010
out of memory 155333 1.165
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We only use a single thread. The approximation can be controlled by a parameter.
UIUC 33David Gleich · Purdue
34. What does our algorithm do?
We extend the framework of Brickell, Dhillon, Sra, and Tropp
on fast solvers for the metric nearness problem.
1. Convert the LPs into an equivalent QP. (improved)
2. Use Dykstra’s projection method to solve the QP.
3. Implement it carefully with a sparse set of dual variables
(active triangle constraints).
4. (new) Uses a convergence criteria based on the KKT
conditions.
UIUC 34David Gleich · Purdue
Richard L.Dykstra
35. We abstract our setup as a LP with an extremely sparse
constraint matrix and use a Dykstra projection method
This is exactly what is proposed in Brickell et al.
The target problem. Is an LP where A has O(n3) rows and x is O(n2) entries
The quadratic program. Is a a smoothed dual with W diagonal, positive. For
! sufficiently large these two are equivalent (a “proximity function”in convex opt)
The dual. Only involves a non-negative vector.
UIUCDavid Gleich · Purdue 35
minimize cT
x subject to Ax b<latexit sha1_base64="RK8zV6OP5umjKF5qt5XqskbhJOU=">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</latexit><latexit sha1_base64="RK8zV6OP5umjKF5qt5XqskbhJOU=">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</latexit><latexit sha1_base64="RK8zV6OP5umjKF5qt5XqskbhJOU=">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</latexit><latexit sha1_base64="RK8zV6OP5umjKF5qt5XqskbhJOU=">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</latexit>
minimize Q(x) = cT
x +
1
2
xT
Wx subject to Ax b
<latexit sha1_base64="uwTLqCGa66SItr3pYBEk2SN7aYQ=">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</latexit><latexit sha1_base64="uwTLqCGa66SItr3pYBEk2SN7aYQ=">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</latexit><latexit sha1_base64="uwTLqCGa66SItr3pYBEk2SN7aYQ=">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</latexit><latexit sha1_base64="uwTLqCGa66SItr3pYBEk2SN7aYQ=">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</latexit>
maximize D(y) = bT
y
1
2
(AT
y + c)T
W 1
(AT
y + c) subject to y 0
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36. Dykstra projection algorithm visits constraints cyclically.
This can be made efficient for Metric LPs
UIUCDavid Gleich · Purdue 36
Input: A 2 RN⇥M
, b 2 RM
, c 2 RN
, > 0, W 2 RN⇥N
(diagonal, positive definite)
Output: ˆx = argminx2A Q(x) where A = {x 2 RN
: Ax b}
y := 0 2 RM
x := W 1
c, k := 0
while not converged do
k := k + 1
(Visit constraints cyclically): i := (k 1) mod M + 1
(Perform correction step): x := x + yi( W 1
ai) where ai is the ith row of A
(Perform projection step): x := x ✓+
i ( W 1
ai) where ✓+
i =
max{aT
i x bi,0}
aT
i W 1ai
(Update dual variables): yi := ✓+
i 0
end while<latexit 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sha1_base64="qRyh3tBhkt79744sgaxMhnukbJg=">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</latexit>
New (kinda). This is a coordinate ascent procedure on the dual for any pd W!
Richard Dykstra (1983),Hildreth (1957),Dax (2003).
Note. These updates only involve the
extremely sparse rows of A
This could be zero!
37. The method works by visiting all constraints and
updating variables.
UIUCDavid Gleich · Purdue 37
This shows the solution
in the matrix, and the
constraint violation in
the cube.
• First it illustrates
what it’s like going
through each triplet
one at a time.
• Then it speeds
things up by only
updating the plot
one time per outer
loop (i.e. the changes
made at all triplets
involving node i)
38. The final algorithm we use stores y as a sparse vector
with the index of the constraints
Choice 1. Store y as a dictionary.
• Easy to implement, enables random access
• Slow to access dictionary
Choice 2. Store an array of the indices where y is non-zero.
• Just push a constraint on to the new non-zero list when we change it.
• Easy to trace through to know when the next non-zero arises.
For our serial code with a specific loop, we use Choice 2 as it’s
considerably faster.
I’ll return to this point later!
UIUCDavid Gleich · Purdue 38
39. We have a variety of technical results that make this
method robust and easy-to-use for specific LPs
We also have a clear understanding of how !, W can give approx. LP solns.
We have good primal-dual theory to certify optimality or near optimality.
We have approximation-related non-optimality. (e.g. if OPT is a two-
approx. to the NP-hard problem, and we get a theta-approx. to the LP, then
we get an OPT+theta approx)
We get fast aposterori approx. bounds for LP too.
UIUCDavid Gleich · Purdue 39
40. On graphs with thousands of nodes,the algorithms for
sparsest cut method take thousands of iterations.
We find sparsest cut is the
hardest problem.
• Total time = 11.5 hrs
• 9.1 billion constraints
• 99.67% are tight.
These are scaled so OPT=0.1
The primal is not always an
upper-bound (iters 1-500)
UIUCDavid Gleich · Purdue 40
Number of Iterations
1000 2000 3000 4000 5000
ConstraintTol/QPScores
-0.05
0
0.05
0.1
0.15
0.2
Polblogs (n = 1222), 1 iter = 4.9 s
0.1*Dual/OPT
0.1*Primal/OPT
0.1 = Scaled OPT
Constraint Tol
41. We use an approach to create correlation clustering
problems from graphs that isn’t λ-CC
• For each pair of nodes (not just edges) , compute Jaccard (i, j) = Ji,j
•
UIUCDavid Gleich · Purdue 41
For this problem,lazy constraint generation for Gurobi helped.
Wang et al.ADMA 2013
Set Si,j = log[(1 + (Ji,j ))/(1 + (Ji,j ))] = 0.05<latexit sha1_base64="RBLV+6hb4MlpuA1+naxY4jFhnmI=">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</latexit><latexit sha1_base64="RBLV+6hb4MlpuA1+naxY4jFhnmI=">AAAFu3icdVRtbxQ3EF4o18L1LbQf+bI0QgrtJd2tippKBCK1QlCBGrgLIGVPqdc7u2fOXm/9Qu6w/Af7D/gXfIVPHd9uSvaSWtrz3HhmnpnHM84bzrRJkreXLn9yZfDpZ1evDT//4suvvt64/s1zLa2icEgll+plTjRwVsOhYYbDy0YBETmHF/n8t3D+4jUozWQ9McsGpoJUNSsZJQZVxxtFZmBh3BhM7MfHjo1e+XgvzrisjuKtNP4h3vqj027HWQHckNu34x///2gaZ39bUnQKDJXsJHeONzZxW634vJB2wmbUrYPj61fuZ4WkVkBtKCdaH6VJY6aOKMMoBz/MrIaG0Dmp4AjFmgjQU7eiw8e3UFPEpVT41SZeaYdnXTCOIsteFGdIbjlRi742l3KOJ9oP+5Cm3J06VjfWQE1bxNLy2Mg4kBwXTAE1fBn3Yc38zXalSDNrQQybv+EsV0QtQ0byRI/0jDSgR5RwOiqZQbtV9hyMm9jSwDMovFNQ3NxNbuYcw561MDOoFEDt3WoLNiczZmDNJucWvAu/Zyz69U3SqQvcheJ6FRxMxqRGNjIFNZxQKQSpC5eVRDC+LKAklhvvMl2eyn0CdCmIwepvnQXTWC0Ue8nOryMqGIIiRRxvEwHMQpchxAqLW1EHdt1D7+4hBphcLpK9bIZblldK2sbTuy6DVtx3flWWIHMgOBfGgBpiW5ZZbRYhjf0W2Onvj7AF70z9qa1EkkLD/A7YewrGS5FL/gDpcG0U7d2fTx57V4f0BPNOeLe6K5yii4xRUay75J1LhxEcxjbXOME2DObFAOsI4wdPAp2nAJO0R73LF95p/hEkGLfe7hFaBg4Ib2bEf0z1r0drN1ZUHBidbbf3dtEJNonGqeuPjej/J7xqNFgcaVnAfxfaNY8Ys0pgEhkGsgoCksty4bJW7891m3iM71txkUd3gC745KTrD8x54flPO2mykz79eXN/t3t8rkY3ou+irSiNfon2o4fRQXQY0eif6F30Pvow2BvQwasBb00vX+p8vo16a2D/BboaA/Y=</latexit><latexit sha1_base64="RBLV+6hb4MlpuA1+naxY4jFhnmI=">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</latexit><latexit sha1_base64="RBLV+6hb4MlpuA1+naxY4jFhnmI=">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</latexit>
Graph |V| |E| # constraints Gurobi Time Dykstra Time Approx
power 4941 6594 6.0 ⇥ 1010
549 s 7.6 hrs 1.07
caGrQc 4158 13422 3.6 ⇥ 1010
out of memory 6.6 hrs 1.33
caHepTh 8638 24806 3.2 ⇥ 1011
out of memory 88.3 hrs 1.34
caHepPh 11204 117619 7.0 ⇥ 1011
out of memory 167.5 hrs 1.27
<latexit 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Wi,j =
(
Si,j + sign(Si,j)" Si,j 6= 0
" + 2"Ind[(i, j) 2 E] Si,j = 0
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42. On graphs with thousands of nodes,the algorithms for
correlation clustering take hundreds of iterations.
The correlation clustering
problems tend to be easier.
• Total time = 7.6 hrs
• 60 billion constraints
• 1.07 approx. LP soln
The jumps are because we
only check constraint tol every
20 steps
• We could have made this parallel and faster
UIUCDavid Gleich · Purdue 42
Number of Iterations
0 50 100 150
-0.2
0
0.2
0.4
0.6
0.8
1
power (n = 4941), 1 iter = 180s
Constraint Tol
Duality Gap
43. These are slow algorithms right now.We tried a variety
of ideas to make them faster.
Parallelization!
• The updates can be done in any order over constraints for the method
to guarantee linear convergence.
• So we could get away with sequentially consistent guarantees.
• Need locking and some fancier techniques to store the updates, but it could work.
• Our worry was about lock-contension (3-number compare exchange?)
• Static schedule for complete 3-regular hypergraphs?
• True–parallel versions of Dykstra algorithm exist, but they average over
solutions computed between processors, because of our large numbers
of constraints, these made little progress.
• Could a hog-wild style approach be adapted to work here?
• We lose coordinate-ascent in a simplistic implementation.
UIUCDavid Gleich · Purdue 43
44. The memory required is still O(n3) in the worst case.
O(n2) memory algs exist,but they are really slow!
Bauschke designed methods that avoid dual-variables for the constraints.
Dykstra is a linearly convergent algorithm. Bauschke’s is asymptotically convergent.
We couldn’t make these competitive.
UIUCDavid Gleich · Purdue 44
Iterations
500 1000 1500 2000 2500
QPobjectivescore
-0.2
0
0.2
0.4
0.6
0.8
1
BauschkeSC on celegansmetabolic
< = 1/10
< = 1/25
< = 1/50
< = 1/100
< = 1/250
< = 1/500
Iterations
500 1000 1500 2000 2500
QPobjectivescore
-0.2
0
0.2
0.4
0.6
0.8
1
DykstraSC on celegansmetabolic
Dual QP
Primal QP
OPT
Bauschke 1996 J.Math Analysis & Approx.
45. A quick summary of other work from our research team
on data-driven scientific computing
Our team’s overall goal is to design algorithms and methods tuned to
the evolving needs and nature of scientific data analysis.
Low-rank methods for network alignment – Huda Nassar
• Principled methods that scale to
aligning thousands of networks.
Spectral properties and generation of realistic
networks – Nicole Eikmeier
• “Power-laws” in the top sing. vals of adj matrix are most
robust than degree “power-laws”
• Fast sampling for hypergraph models with higher-order structure.
Local analysis of network data – Meng Liu
• Applications in bioinformatics, software https://github.com/kfoynt/LocalGraphClustering
UIUCDavid Gleich · Purdue 45
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Fig. 5. For a Kronecker graph with a 2 ⇥ 2 initi
been “⌦-powered” three times to an 8 ⇥ 8 probability
46. We have extensively explored principled methods in
terms of higher-order and multi-way data.
The key question. Much of the data now collected and curated has rich
multi-way and higher-order structure. How can we engineer algorithms
with guarantees that capture the structure?
UIUCDavid Gleich · Purdue 46
Figure 1: An illustration of Markov chain methods and our
proposed RHOMP model.
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Multiway structure in sequence
modeling for prediction
Higher-order structures in
networks
Benson,Gleich,Leskovec (2015,2016)
Klymko,Gleich,Kolda (2014)
Mohammadi,Gleich,Kolda,Grama (2017)
Higher-order methods for data
Yu,Gleich,Lim (SIMAX 2015)
Benson,Gleich,Lim (SIAM Review 2017)
Wu,Benson,Gleich (2016) Wu,Gleich (2017)
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47. UIUC
Papers. arXiv: 1712.05825 (at WWW2018),1806.01678
Software. github: nveldt/LamCC,nveldt/MetricOptimization
47
A different framework for clusters communities in graphs. (LAMBDACC)
An improved procedure to solve LPs with metric constraints
Issues.
• Links with other approaches
such as cut-matching games?
• Would love to solve problems with 100k
node graphs J can we get there with
parallel / distributed settings?
With Nate Veldt (Purdue),
Tony Wirth (Melbourne),
and James Saunderson (Monash)
David Gleich · Purdue