This document discusses geometric routing in sensor networks and introduces an important open conjecture. It provides contributions towards bounding the failure of greedy routing and characterizing when it succeeds. Greedy routing works by recursively selecting the neighbor node closest to the destination but can fail when no such node exists, motivating the conjecture about when it is guaranteed to succeed.
A session from Qubole Best Practice Webinar Series- “Big Data Secrets from the Pros”. Covers how to make Apache Hive queries run faster by
a. Better layout of data on HDFS via partitioning and bucketing
b. Designing test queries by using block and bucket sampling before running the queries on large datasets
c. Using bucket map joins and parallel processing to run queries faster
Visit www.qubole.com for more information.
A session from Qubole Best Practice Webinar Series- “Big Data Secrets from the Pros”. Covers how to make Apache Hive queries run faster by
a. Better layout of data on HDFS via partitioning and bucketing
b. Designing test queries by using block and bucket sampling before running the queries on large datasets
c. Using bucket map joins and parallel processing to run queries faster
Visit www.qubole.com for more information.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
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GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
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LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
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Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
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UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
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GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
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People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
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All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
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Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
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Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
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Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
20240609 QFM020 Irresponsible AI Reading List May 2024
Greedy embedding problem
1. Introduction Conjecture Contribution Bound Characterization
Geometric Routing: Theoretical Foundations, an
Important Conjecture and Some Progress
Subhas K. Ghosh
March 2, 2010
Subhas K. Ghosh Greedy Embedding
2. Introduction Conjecture Contribution Bound Characterization Geometric routing Greedy routing Greedy routing fails! Rubber band
Sensor Networks and Ad hoc networks
Subhas K. Ghosh Greedy Embedding
3. Introduction Conjecture Contribution Bound Characterization Geometric routing Greedy routing Greedy routing fails! Rubber band
Geometric routing
Ad hoc networks and Distributed Wireless Sensor Networks: no
universally known system of addresses.
Resource limitations: prohibitive to store and maintain large forwarding
tables.
Geometric routing: use geographic coordinates of the nodes as
addresses.
Simplest of routing strategy: recursively select a vertex that is closer to
destination than current vertex.
Point-to-point routing service: enable data centric storage, a Distributed
Hash Table (DHT) is implemented in the sensor network, and data is
stored at the nodes of the network using the DHT.
Subhas K. Ghosh Greedy Embedding
4. Introduction Conjecture Contribution Bound Characterization Geometric routing Greedy routing Greedy routing fails! Rubber band
Geometric routing
Greedy routing
set i := 0, v0 := s
while vi = t do
du := minu∈N (vi ) {d (u, t) : d (u, t) < d (vi , t)}
if ∃u then
set i := i + 1, vi := u
else
fail!
end if
end while
Subhas K. Ghosh Greedy Embedding
9. Introduction Conjecture Contribution Bound Characterization Geometric routing Greedy routing Greedy routing fails! Rubber band
Greedy routing sometimes fails!
Greedy routing sometimes fails to deliver a packet because of the
phenomenon of “voids” (nodes with no neighbor closer to the destination).
This is essentially reaching a local minimum with respect to the distance
functional defined by s, t -pair.
Alternative: face routing [KK00].
Subhas K. Ghosh Greedy Embedding
10. Introduction Conjecture Contribution Bound Characterization Geometric routing Greedy routing Greedy routing fails! Rubber band
Geometric routing is complicated
It is unlikely that future ad hoc networks can rely on the availability of
precise geographic coordinates (GPS is costly and does not work in all
situation).
Importantly, the precise coordinates may be disadvantageous as they do
not account for obstructions or other topological properties of the
network.
How to retain the benefits of geographic routing in the absence of location
information?
Rao, Papadimitriou, Shenker and Stoica (in [RPSS03]) defined a scalable
coordinate-based routing algorithm that does not rely on location information,
and thus can be used in a wide variety of ad hoc and sensor-nets
environments.
Subhas K. Ghosh Greedy Embedding
11. Introduction Conjecture Contribution Bound Characterization Geometric routing Greedy routing Greedy routing fails! Rubber band
Virtual Coordinates
In [RPSS03] the nodes first decide on fictitious virtual coordinates in R2 , and
then apply greedy routing based on those. The coordinates are found by a
distributed version of the rubber band algorithm originally due to Tutte [Tut60].
Note that embedding in higher dimension requires storing more
information per node.
On the basis of extensive experimentation in [RPSS03] authors showed
that this approach makes greedy routing much more reliable (works
97.5% cases).
Kleinberg proved 100% success rate by assigning virtual coordinates in
the hyperbolic plane rather than the Euclidean plane.
Subhas K. Ghosh Greedy Embedding
12. Introduction Conjecture Contribution Bound Characterization Geometric routing Greedy routing Greedy routing fails! Rubber band
Graph Embedding
An embedding of an undirected graph G in a metric space (X, d) is a mapping
e : V (G) → X.
1 In this work we will be concerned with a special case when X is the plane
R2 with the Euclidean (i.e. 2 ) metric.
2 The function e then maps each edge of the graph G to the line-segments
joining the images of its end points.
Subhas K. Ghosh Greedy Embedding
13. Introduction Conjecture Contribution Bound Characterization Geometric routing Greedy routing Greedy routing fails! Rubber band
Planar Embedding
An undirected graph G is a planar graph if it can be drawn on a plane so that
no edges intersect.
1 Since we are concerned with R2 we can say that embedding e is planar
when no two line-segments on the embedded graph intersect at any point
other than their end points.
Subhas K. Ghosh Greedy Embedding
14. Introduction Conjecture Contribution Bound Characterization Geometric routing Greedy routing Greedy routing fails! Rubber band
Required Defn.
Let G = (V, E) be a finite undirected graph with vertex set V (G) and edge set
E(G).
1 A connected acyclic subgraph T of G is a tree. If V (T ) = V (G), then T is
a spanning tree.
2 For x, y ∈ V (G), xy-paths P and Q in G are internally (vertex) disjoint or
openly disjoint if V (P ) ∩ V (Q) = {x, y}.
3 Let p(x, y) denote the maximum number of pair-wise internally disjoint
paths between x, y ∈ V (G).
4 A nontrivial graph G is k-connected if p(u, v) ≥ k for any two distinct
vertices u, v ∈ V (G).
5 The connectivity κ(G) of G is the maximum value of k for which G is
k-connected.
Subhas K. Ghosh Greedy Embedding
15. Introduction Conjecture Contribution Bound Characterization Geometric routing Greedy routing Greedy routing fails! Rubber band
Tutte Embedding: Rubber band representation
Let G = (V, E) be a connected graph and ∅ = S ⊆ V . Fix a map x0 : S → R2 .
We extend this to a map x : V → R2 (a geometric representation of G, or a
drawing on paper) as follows:
1 Replace the edges by ideal rubber bands (satisfying Hooke’s Law). Think
of the nodes in S as nailed to their given position (node u ∈ S to
x0 (u) ∈ R2 ), but let the other nodes settle in equilibrium.
2 We’ll see that this equilibrium position is uniquely determined.
3 We call it the rubber band representation of G in R2 extending x0 .
Subhas K. Ghosh Greedy Embedding
16. Introduction Conjecture Contribution Bound Characterization Geometric routing Greedy routing Greedy routing fails! Rubber band
Tutte Embedding: Rubber band representation
To be precise, let x(u) ∈ R2 be the position of node u ∈ V . By definition,
x(u) = x0 (u), ∀u ∈ S. The energy of this representation can be defined as
2
E(x) = |x(u) − x(v)| .
uv∈E
We want to find the representation with minimum energy, subject to the
boundary conditions:
minimize E(x) s.t. x(u) = x0 (u), ∀u ∈ S.
Subhas K. Ghosh Greedy Embedding
17. Introduction Conjecture Contribution Bound Characterization Geometric routing Greedy routing Greedy routing fails! Rubber band
Tutte Embedding: Rubber band representation
1 If S = ∅, then the function E(x) is strictly convex, as
2 2
E(x) = uv∈E |x(u) − x(v)| = uv∈E k=1 (xk (u) − xk (v))2 .
2 If any of the x(u) tends to infinity, then E(x) tends to infinity ⇒ the
representation with minimum energy is uniquely determined.
3 If u ∈ V S, then at the minimum point the partial derivative of E(x) with
respect to any coordinate of x must be 0 ⇒ v∈neigh(u) (x(u) − x(v)) = 0
4 This equation means that every free node is in the center of gravity of its
neighbors.
Subhas K. Ghosh Greedy Embedding
18. Introduction Conjecture Contribution Bound Characterization Geometric routing Greedy routing Greedy routing fails! Rubber band
Tutte Embedding
Every 3-connected planar graph has a convex embedding in the the Euclidean
plane (using Tutte’s rubber band algorithm [Tut60]).
Theorem
Let G = ({1, ·, n} , E) be a 3-connected, planar graph that has a face
(k + 1, . . . , n) for some k < n. Let pk+1 , . . . , pn be the vertices (in this order) of
a convex (n − k)-gon. Let w : E → R+ be an assignment of positive weights
to the internal edges. Then:
There are unique positions p1 , . . . , pk ∈ R2 for the interior vertices such
that all interior vertices are in equilibrium.
All internal faces of G are then realized as non-overlapping convex
polygons.
Subhas K. Ghosh Greedy Embedding
19. Introduction Conjecture Contribution Bound Characterization Geometric routing Greedy routing Greedy routing fails! Rubber band
Tutte Embedding: example
Subhas K. Ghosh Greedy Embedding
20. Introduction Conjecture Contribution Bound Characterization Geometric routing Greedy routing Greedy routing fails! Rubber band
Virtual coordinate: Foundation?
Despite the solid grounding of the ideas in geometric graph theory, no
theoretical results and guarantees were known for such schemes.
Subhas K. Ghosh Greedy Embedding
21. Introduction Conjecture Contribution Bound Characterization Greedy Embedding Conjecture Conjecture - Details Known results
Greedy Embedding
Let d (pu , pv ) denote the Euclidean distance between two points pu and pv .
Definition
Greedy embedding ([PR05]): A greedy embedding x of a graph G = (V, E)
into a metric space (X, d) is a function x : V (G) → X with the following
property: for every pair of non-adjacent vertices s, t ∈ V (G) there exists a
vertex u ∈ V (G) adjacent to s such that d (x (u) , x (t)) < d (x (s) , x (t)).
Subhas K. Ghosh Greedy Embedding
22. Introduction Conjecture Contribution Bound Characterization Greedy Embedding Conjecture Conjecture - Details Known results
Greedy Embedding : Conjecture(Weak)
This notion of greedy embedding was defined by Papadimitriou and Ratajczak
in [PR05]. They have presented graphs which does not admit a greedy
embedding in the Euclidean plane, and conjectured following:
Conjecture
(Weak). Every 3-connected planar graph has a greedy embedding in the
Euclidean plane.
Subhas K. Ghosh Greedy Embedding
23. Introduction Conjecture Contribution Bound Characterization Greedy Embedding Conjecture Conjecture - Details Known results
Greedy Embedding : Conjecture (Strong)
A convex embedding of a planar graph is a planar embedding with a property
that all faces, including the external faces are convex. Additionally,
Papadimitriou and Ratajczak stated the following stronger form of the
conjecture:
Conjecture
(Strong). Every 3-connected planar graph has a greedy convex embedding in
the Euclidean plane.
Note that every 3-connected planar graph has a convex embedding in the the
Euclidean plane (using Tutte’s rubber band algorithm [Tut60]).
Subhas K. Ghosh Greedy Embedding
24. Introduction Conjecture Contribution Bound Characterization Greedy Embedding Conjecture Conjecture - Details Known results
Both hypothesis are necessary
Proposition
Kk,5k+1 has no greedy embedding for k > 0.
v6
v1
v5
r
v2
π
≤ 3
v3
v4
Figure: K1,6 has no greedy embedding
Subhas K. Ghosh Greedy Embedding
25. Introduction Conjecture Contribution Bound Characterization Greedy Embedding Conjecture Conjecture - Details Known results
Both hypothesis are necessary
These counterexamples imply that the hypotheses of the conjecture are
necessary, in that there exist counterexamples that are planar but not
3-connected (K2,11 ), or 3-connected but not planar (K3,16 ); also, they show
that high connectivity alone does not guarantee a greedy embedding.
Subhas K. Ghosh Greedy Embedding
26. Introduction Conjecture Contribution Bound Characterization Greedy Embedding Conjecture Conjecture - Details Known results
Implications of the conjecture being true
There exists a way to assign virtual coordinates to a large class of graphs
where greedy routing is guaranteed.
Since adding edges only improves the embeddability of a graph, the
conjecture extends immediately to any graph with a 3-connected planar
subgraph. Hence to a even larger class.
Subhas K. Ghosh Greedy Embedding
27. Introduction Conjecture Contribution Bound Characterization Greedy Embedding Conjecture Conjecture - Details Known results
Known results
Recently, greedy embedding conjecture has been proved in [LM08]. In [LM08]
authors construct a greedy embedding into the Euclidean plane for all circuit
graphs – which is a generalization of 3-connected planar graphs. Similar
result was independently discovered by Angelini, Frati and Grilli [AFG08].
Theorem
([LM08]) Any 3-connected graph G without having a K3,3 minor admits a
greedy embedding into the Euclidean plane.
Subhas K. Ghosh Greedy Embedding
28. Introduction Conjecture Contribution Bound Characterization Open Problem Weak greedy
Open Problem
The greedy embedding algorithm presented in [LM08, AFG08] not necessarily
produce a convex greedy embedding, and in fact the embedding may not
even be a planar one. In this work we consider the convex greedy embedding
conjecture. Other questions that we can ask:
1 What is the least dimension of a normed vector space V where every
graph with n nodes has a greedy embedding?
2 Why Tutte embedding improves delivery success?
Subhas K. Ghosh Greedy Embedding
29. Introduction Conjecture Contribution Bound Characterization Open Problem Weak greedy
Contribution
Given a 3-connected planar graph G = (V, E),
1 An embedding x : V → R2 of G is a planar convex greedy embedding if
and only if, in the embedding x, weight of the maximum weight spanning
tree (wt(T )) and weight of the minimum weight spanning tree (wt(MST))
1−δ
satisfies wt(T )/wt(MST) ≤ (|V | − 1) , for some 0 < δ ≤ 1.
√
2 G has a β-weak greedy convex embedding in R2 with β ∈ [1, 2 2 · d(G)].
Also, this bound is achieved by Tutte embedding.
3 We show a possibility that Tutte embedding with correct choice of edge
weights may produce greedy embedding of G.
Subhas K. Ghosh Greedy Embedding
30. Introduction Conjecture Contribution Bound Characterization Open Problem Weak greedy
Weak greedy embedding
In order to obtain this result we consider a weaker notion of greedy
embedding. Weak greedy embedding allows path finding algorithm to
proceed as long as local optima is bounded by a factor. Formally,
Definition (Weak greedy embedding)
Let β ≥ 1. A β–weak greedy embedding x of a graph G = (V, E) is a planar
embedding of G with the following property: for every pair of non-adjacent
vertices s, t ∈ V (G) there exists a vertex u ∈ V (G) adjacent to s such that
d (x (u) , x (t)) < β · d (x (s) , x (t)).
Subhas K. Ghosh Greedy Embedding
31. Introduction Conjecture Contribution Bound Characterization Open Problem Weak greedy
Weak greedy routing
Algorithm WEAK − GREEDY (s, t, β)
if s = t then
return success.
else
∆
B = {v : (s, v) ∈ E and d(v, t) < β · d(s, t)}.
if B = ∅ then
return failure.
else
∀v ∈ B: WEAK − GREEDY (v, t, β).
end if
end if
Subhas K. Ghosh Greedy Embedding
32. Introduction Conjecture Contribution Bound Characterization Trees of all kind ... and a Theorem
Outline
For vertex s define βs : min∀t∈V −{s} at least one branch of this recursive
procedure returns success.
H(s, βs ) ⊆ G induced by all vertices and edges of βs –weak greedy
st–paths for all possible terminal vertex t ∈ V − {s}.
Let T (s, βs ) be any spanning tree of H(s, βs ). T (s, βs ) has unique
βs –weak greedy st–paths for all possible terminal vertex t ∈ V − {s} from
s.
We will call Ts = T (s, βs ) optimal weak greedy tree w.r.t vertex s.
Define βmax = maxs∈V {βs }.
In following our objective will be to obtain a bound on βmax for any
3-connected planar graph G under embedding x.
To obtain this bound we will use the properties of weak greedy trees.
Subhas K. Ghosh Greedy Embedding
33. Introduction Conjecture Contribution Bound Characterization Trees of all kind ... and a Theorem
Bounding the weight of trees: Outline
In the planar convex embedding of G, let weight of an edge e = uv be its
length i.e. wt(e) = d(u, v).
Define wt(T (s, βs )) = e∈E(T (s,βs )) wt(e).
We obtain a lower and upper bound on the weight of T (s, βs ).
On the other hand we also obtain a upper bound on the weight of any
spanning tree T of G in its embedding wt(T ), and
a lower bound on the weight of any minimum spanning tree MST of G,
wt(MST).
Subhas K. Ghosh Greedy Embedding
34. Introduction Conjecture Contribution Bound Characterization Trees of all kind ... and a Theorem
Bounding the weight of trees: Outline...
Surely wt(MST) ≤ wt(Ts ) ≤ wt(T ), and from this we derive an upper and
a lower bound on βmax . Let dmax (G) = maxu,v∈V d(u, v) be the diameter
of G, and let minimum edge length in embedding of G be dmin (G).
√
We derive that, wt(T ) ≤ 2 · (|V | − 1) · dmax (G).
We also show that, dmax (G) ≤ wt(MST) ≤ 2.5 · d2 (G).
max
Subhas K. Ghosh Greedy Embedding
35. Introduction Conjecture Contribution Bound Characterization Trees of all kind ... and a Theorem
Bounding the weight of trees: Outline...
Definition (Increasing and decreasing sequence)
For a βs –weak greedy st–path Pst = {s = u0 , u1 , . . . , uk = t}, an ordered
vertex sequence {ui0 , . . . , uir } of Pst is an increasing sequence of length r if
d(ui0 , t) ≤ . . . ≤ d(uir , t) holds. Similarly, an ordered sequence of vertices
{ui0 , . . . , uir } of Pst is a decreasing sequence of length r if
d(ui0 , t) ≥ . . . ≥ d(uir , t) holds. Usually, we will refer any maximal (by property
of monotonically non-decreasing or non-increasing) sequence of vertices as
increasing or decreasing sequence.
It is straightforward to observe that if an st–path is βs –weak greedy for βs > 1,
then it has a monotonically non-decreasing sequence of vertices. However,
every st–path must have a trailing monotonically decreasing sequence that
reaches t.
Subhas K. Ghosh Greedy Embedding
36. Introduction Conjecture Contribution Bound Characterization Trees of all kind ... and a Theorem
Bounding the weight of trees: Outline...
We will call an increasing sequence {ui0 , . . . , uir } of Pst a β-increasing
sequence of length r if it is maximal and for j = 1, . . . , r, d(uij , t) ≤ βd(uij−1 , t)
holds (with equality for at least one j). We will denote it as inc(r, d, β), where d
indicates d(ui0 , t) = d.
Lemma
Let inc(k, d, β) = {ui0 , . . . , uik } be a β-increasing sequence of length k from a
βs –weak greedy st–path such that d(ui0 , t) = d. Then
β+1
d(β k − 1) ≤ wt(inc(k, d, β)) ≤ d(β k − 1)
β−1
Where wt(inc(k, d, β)) is the sum of the weight of the edges of inc(k, d, β).
Subhas K. Ghosh Greedy Embedding
37. Introduction Conjecture Contribution Bound Characterization Trees of all kind ... and a Theorem
Bounding the weight of trees: Outline...
Lemma
Let dec(k, d, γ) = {ui0 , . . . , uik } be a γ-decreasing sequence of length k such
that d(ui0 , t) = d. Then
1 1
d(1 − ) ≤ wt(dec(k, d, γ)) ≤ dk(1 + )
γ γ
Lemma
Let P (k, β) be a k length β–weak greedy st–path such that t is a leaf vertex of
the tree Ts . Then
βk − 1
dmin (G) · k · (β − 1) ≤ wt(P (k, β)) ≤ 2 · dmax (G) ·
β−1
Subhas K. Ghosh Greedy Embedding
38. Introduction Conjecture Contribution Bound Characterization Trees of all kind ... and a Theorem
Bounding the weight of trees: Outline...
Finally we derive upper and lower bounds on the the weight of T (s, βs ) as:
|V |−1
βmax − 1
dmin (G) (βmax − 1) (|V | − 1) ≤ wt(Ts ) ≤ 2dmax (G)
βmax − 1
Using wt(MST) ≤ wt(Ts ) ≤ wt(T ), we have:
|V |−1
βmax − 1
dmax (G) ≤ wt(MST) ≤ wt(Ts ) ≤ 2dmax (G)
βmax − 1
Or,
|V |−1
βmax − 1 1
≥
βmax − 1 2
And this holds for any βmax > 1 when |V | ≥ 3.
Subhas K. Ghosh Greedy Embedding
39. Introduction Conjecture Contribution Bound Characterization Trees of all kind ... and a Theorem
Bounding the weight of trees: Outline...
Using wt(MST) ≤ wt(Ts ) ≤ wt(T ), we also have:
√
dmin (G) (βmax − 1) (|V | − 1) ≤ wt(Ts ) ≤ wt(T ) ≤ 2 · (|V | − 1) · dmax (G)
Now using d(G) = dmax (G)/dmin (G) we have:
√ dmax (G) √ √
βmax ≤ 2· + 1 ≤ 2 · d(G) + 1 ≤ 2 2 · d(G)
dmin (G)
Subhas K. Ghosh Greedy Embedding
40. Introduction Conjecture Contribution Bound Characterization Trees of all kind ... and a Theorem
Bounding the weight of trees: Outline...
It follows
Theorem
Let G = (V, E) be any three connected planar√graph. Then G has a β-weak
greedy convex embedding in R2 with β ∈ [1, 2 2 · d(G)]. Also, this bound is
achieved by Tutte embedding.
Subhas K. Ghosh Greedy Embedding
41. Introduction Conjecture Contribution Bound Characterization Trees of all kind ... and a Theorem
However, this is not very good
F G F G
C
B C
B D
A D
A
E H E H
(a) (b)
Figure: Illustration of Tutte embedding of a cube: (a)Equal edge weights, (b)Unequal
edge weights
√
β ∈ [1, 2 2 · d(G)]
In Tutte embedding of a 3–connected planar graph G with arbitrary weights on
the edges, bound on β depends entirely on the choice of the edge weights in
the Tutte embedding.
Subhas K. Ghosh Greedy Embedding
42. Introduction Conjecture Contribution Bound Characterization When weights are close When weights must be close
When weight of T and MST are close
Theorem (If weights are close it must be greedy)
For sufficiently large |V | for a 3-connected planar graph G = (V, E) if
embedding x : V → R2 of G is such that the maximum weight spanning tree
(T ) and minimum weight spanning tree (MST) satisfies,
1−δ
wt(T )/wt(MST) ≤ (|V | − 1) , for some 0 < δ ≤ 1, then embedding x is a
convex greedy embedding of G.
Subhas K. Ghosh Greedy Embedding
43. Introduction Conjecture Contribution Bound Characterization When weights are close When weights must be close
When weight of T and MST are close
Proof: wt(MST) ≤ wt(Ts ) ≤ wt(T ) and wt(MST) > 0,
2 · dmin (G) · (βmax − 1) · (|V | − 1) wt(T )
≤
5 · d2 (G)
max wt(MST)
5 · dmax (G) · d(G) wt(T )
And hence, βmax ≤ · +1
2 · (|V | − 1) wt(MST)
then for sufficiently large |V |, βmax → 1 from above (note that βmax > 1).
Subhas K. Ghosh Greedy Embedding
44. Introduction Conjecture Contribution Bound Characterization When weights are close When weights must be close
More interesting direction
Theorem (If G is greedy then weights are close)
Given a 3-connected planar graph G = (V, E), if embedding x : V → R2 of G
is a convex greedy embedding then in embedding x the maximum weight
spanning tree (T ) and minimum weight spanning tree (MST) satisfies:
1−δ
wt(T )/wt(MST) ≤ (|V | − 1) , for some 0 < δ ≤ 1.
Subhas K. Ghosh Greedy Embedding
45. Introduction Conjecture Contribution Bound Characterization When weights are close When weights must be close
Proof outline
Fact
A graph is 3-connected and planar if and only if each edge is in exactly two
non-separating induced cycles [Kel78]
1 Consider two cases: (Case - 1) e is on two internal faces F and F , and
(Case - 2) e is on the boundary face (infinite face is the other one).
2 Let us also assume that wt(T )/wt(MST) ≥ (|V | − 1). W.l.o.g. let
wt(MST) = 1. Since T is a spanning tree it has (|V | − 1) edges, and
hence has at least one edge e ∈ T of weight wt(e) ≥ 1.
Subhas K. Ghosh Greedy Embedding
46. Introduction Conjecture Contribution Bound Characterization When weights are close When weights must be close
Proof outline...
For a graph G, a thread is a path P of G such that any degree 2 vertex x of G
is not an end vertex of P . A sequence S = (G0 , {xi Pi yi : i = 1, . . . , k}) is an
ear-decomposition of G if:
1 G0 is a subdivision of K4 ,
2 xi Pi yi is a path with end-vertices xi and yi such that Gi = Gi−1 ∪ Pi is a
subgraph of G, and Gi−1 ∩ Pi = {xi , yi }, but xi , yi do not belong to a
common thread of Gi−1 for i = 1, . . . , k, and
3 Gk = G.
Subhas K. Ghosh Greedy Embedding
47. Introduction Conjecture Contribution Bound Characterization When weights are close When weights must be close
Proof outline...
Lemma
Let G be a 3–connected graph, e = uv ∈ E(G). Let C1 and C2 be
non-separating cycles of G such that C1 ∩ C2 = uev. Then there exists an
ear-decomposition of G such that C1 ∪ C2 ⊂ G0 .
Subhas K. Ghosh Greedy Embedding
48. Introduction Conjecture Contribution Bound Characterization When weights are close When weights must be close
Proof outline...
Say, e = uv is on two internal faces F1 and F2 . Consider a vertex u from face
F1 and another vertex v from face F2 . First consider K4 , which has four
faces, and exactly one planar convex embedding. However, vertices u, v, u , v
must be spanned by the MST using exactly 3 edges. If e is chosen in the MST
then other edges are of length 0, as wt(e) ≥ 1 and wt(MST) = 1. If e is not
selected in MST - then it can be easily seen that either wt(MST) > 1, or the
drawing is not planar - a contradiction.
u u u
v
v v v u
K4 Planar convex embedding of K4
Figure: Illustration to the proof of Case - 1 for K4
Subhas K. Ghosh Greedy Embedding
49. Introduction Conjecture Contribution Bound Characterization When weights are close When weights must be close
Concluding remarks
With our result, and the example presented above we can ask following
question:
For every 3–connected planar graph G, is it possible to choose edge weights
in the Tutte embedding such that we obtain a greedy convex embedding?
We believe that answer to this question will help in resolving convex greedy
embedding conjecture of Papadimitriou and Ratajczak.
Subhas K. Ghosh Greedy Embedding
51. Appendix Reference
Reference: I
Patrizio Angelini, Fabrizio Frati, and Luca Grilli.
An algorithm to construct greedy drawings of triangulations.
In 16th International Symposium on Graph Drawing (GD ’08), 2008.
To appear.
Alexander Kelmans.
The concept of a vertex in a matroid, the non-separating cycles, and a new criterion for graph planarity.
In Algebraic Methods in Graph Theory, Colloq. Math. Soc. Janos Bolyai, (Szeged, Hungary, 1978)NorthHolland, 1:345–388, 1978.
Brad Karp and H. T. Kung.
GPSR: greedy perimeter stateless routing for wireless networks.
In MobiCom ’00: Proceedings of the 6th annual international conference on Mobile computing and networking, pages 243–254, New York, NY,
USA, 2000. ACM Press.
Tom Leighton and Ankur Moitra.
Some results on greedy embeddings in metric spaces.
In FOCS ’08: Proceedings of the 49th Annual IEEE Symposium on Foundations of Computer Science (FOCS’08), Washington, DC, USA, 2008.
IEEE Computer Society.
Christos H. Papadimitriou and David Ratajczak.
On a conjecture related to geometric routing.
Theor. Comput. Sci., 344(1):3–14, 2005.
Ananth Rao, Christos Papadimitriou, Scott Shenker, and Ion Stoica.
Geographic routing without location information.
In MobiCom ’03: Proceedings of the 9th annual international conference on Mobile computing and networking, pages 96–108, New York, NY, USA,
2003. ACM Press.
W. T. Tutte.
Convex Representations of Graphs.
Proc. London Math. Soc., s3-10(1):304–320, 1960.
Subhas K. Ghosh Greedy Embedding