Short version of Dominating Sets, Multiple Egocentric Networks and Modularity...Moses Boudourides
Short version of Dominating Sets, Multiple Egocentric Networks and Modularity Maximizing Clustering of Social Networks. By M.A. Boudourides & S.T. Lenis
Strong (Weak) Triple Connected Domination Number of a Fuzzy Graphijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Equi independent equitable domination number of cycle and bistar related graphsiosrjce
IOSR Journal of Mathematics(IOSR-JM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Here, we look at the problem of going from a source s to a possible multiple destinations. At them, each of the Lemmas, Theorems and Corollaries used to prove the properties of the
1. Bellman-Ford
2. Dijkstra
are examined in detail.
Short version of Dominating Sets, Multiple Egocentric Networks and Modularity...Moses Boudourides
Short version of Dominating Sets, Multiple Egocentric Networks and Modularity Maximizing Clustering of Social Networks. By M.A. Boudourides & S.T. Lenis
Strong (Weak) Triple Connected Domination Number of a Fuzzy Graphijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Equi independent equitable domination number of cycle and bistar related graphsiosrjce
IOSR Journal of Mathematics(IOSR-JM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Here, we look at the problem of going from a source s to a possible multiple destinations. At them, each of the Lemmas, Theorems and Corollaries used to prove the properties of the
1. Bellman-Ford
2. Dijkstra
are examined in detail.
О формировании кластера непрерывного казачьего образования. Иванова Валентин...TCenter500
О формировании кластера непрерывного казачьего образования.
Иванова Валентина Николаевна, Ректор Московского государственного университета технологий и управления имени К.Г. Разумовского
О Фестивале юных экологов и туристов, приуроченном к открытию фотовыставки «В...TCenter500
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Моргун Дмитрий Владимирович, директор МДЮЦ экологии, краеведения и туризма
A graph G consists of a non empty set V called the set of nodes (points, vertices) of the graph, a set E, which is the set of edges of the graph and a mapping from the set of edges E to a pair of elements of V.
Any two nodes, which are connected by an edge in a graph are called "adjacent nodes".
In a graph G(V,E) an edge which is directed from one node to another is called a "directed edge", while an edge which has no specific direction is called an "undirected edge". A graph in which every edge is directed is called a "directed graph" or a "digraph". A graph in which every edge is undirected is called an "undirected graph".
If some of edges are directed and some are undirected in a graph then the graph is called a "mixed graph".
Any graph which contains some parallel edges is called a "multigraph".
If there is no more than one edge but a pair of nodes then, such a graph is called "simple graph."
A graph in which weights are assigned to every edge is called a "weighted graph".
In a graph, a node which is not adjacent to any other node is called "isolated node".
A graph containing only isolated nodes is called a "null graph". In a directed graph for any node v the number of edges which have v as initial node is called the "outdegree" of the node v. The number of edges to have v as their terminal node is called the "Indegree" of v and Sum of outdegree and indegree of a node v is called its total degree.
Graphs are propular to visualize a problem . Matrix representation is use to convert the graph in a form that used by the computer . This will help to get the efficent solution also provide a lots of mathematical equation .
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...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.
2. What is a Graph?
A data structure that consists of a set of nodes
(vertices) and a set of edges that relate the nodes to
each other
The set of edges describes relationships among the
vertices
3. A graph G consists of two things:
1.A set V of elements called nodes(or points or vertices)
2.A set E of edges such that each edge e in E is identified with a unique pair
[u,v] of nodes in V, denoted by e=[u,v]
Suppose e=[u,v]. Then the nodes u and v are called endpoints of e and u and v
are said to be adjacent nodes or neighbors.
5. Degree of a graph
The degree of a node u, written as deg(u), is the
number of edges containing u.
If deg(u)=0 that is, if u does not belong to any
edge then u is called an isolated node.
A
B C
D
E
Nodes degree
A 4
B 3
C 2
D 3
E 4
F 0F
Isolated node
6. Directed Graph
Each edge is assigned a direction or each edge E is
identified with ordered pair (u,v)
(u,v) directed graph
[u,v] undirected graph
U=origin
v=destination
7. Connected Graph
A graph G is said to be connected graph if
there is atleast one path between every pair
of vertices in G.
A graph which is not connected is called
disconnected graph.
8. Strongly Connected Graph
A directed graph G is said to be
strongly connected if foreach pairu,v
of nodes in G there is a path fromu to
v and there is also a path fromv to u.
10. In simple words,
A strongly connected component (SCC) of a
directed graph is a maximal strongly connected subgraph.
11.
12. Depth-First-Search
(DFS)
The idea behind DFS is travel as far as
you can down a path
DFS can be implemented efficiently using
a stack .
This algorithm is similar to inorder
traversal of a binary tree i.e. first of all we
process the left node then root and then
the right node.
14. Kosaraju's algorithm
Kosaraju's algorithm (also known as
the Kosaraju–Shariralgorithm) is a linear
time algorithm to find the strongly
connected components of a directed
graph.
It makes use of the fact that
the transpose graph (the same graph with
the direction of every edge reversed) has
15. Algorithm
1) Let G be a directed graph and S be an empty stack.
2) Perform a depth-first search starting from any v.
3) Reverse the directions of all arcs to obtain the transpose
graph.
4) Again perform depth-first search on transpose graph.
17. A group of people are generally strongly connected (For example,
students of a class or any other common place). Many people in
these groups generally like some common pages or play common
games. The SCC algorithms can be used to find such groups and
suggest the commonly liked pages or games to the people in the
group who have not yet liked commonly liked a page or played a
game.
Use of SCC algorithm in Social life
18. MultigraphMultigraph
A graph G is said to be a multigraph if itA graph G is said to be a multigraph if it
has:has:
1.1. multiple edgesmultiple edges
2.2. LoopsLoops
A
B C
D
e1
e7 e6
e5
e4
e3
e2
19. Directed Graph
A directed graph G also called a digraph
or graph is the same as multigraph
except that each edge in G is assigned a
direction.
Outdegree= number of outgoing edges
Indegree= number of incoming edges
20. U- origin
V - destination
A
B
C
D
E
Nodes Indegree outdegree
A 0 4
B 1 1
C 2 1
D 3 0
E 1 1
Directed Graph
21. Connected Graph
A graph G is said to be connected graph if
there is atleast one path between every pair
of vertices in G.
A graph which is not connected is called
disconnected graph.
22. Articulation PointArticulation Point
An articulation point in a connected graph
is a vertex that, if delete would break the
graph into two or more pieces (connected
components).
25. Biconnected GraphBiconnected Graph
A graph with no articulation point is called
biconnected graph.
In other words, a graph is biconnected if
and only if any vertex is deleted, the graph
remains connected.
27. Biconnected Components
A biconnected component of a graph is a
maximal biconnected subgraph.
A biconnected subgraph that is not
properly contained in a larger biconnected
subgraph.
33. Let G be a directed graph with m nodes
v1,v2…….,vm. Suppose we want to find
the path matrix P of the graph G.
Warshall gave an alogrithm for this
purpose that is much more efficient than
calculating the powers of the adjacency
matrix A
Warshall’s Algorithm: Shortest Paths
34. Where adjacency matrix A =(aij) of the graph G is m x m
matrix defined as follows:
aij= 1 If vi is adjacent to vj, i.e. if there is an edge (vi,vj)
0 otherwise
such a matrix A, which contains entries of only 0 and 1, is called a
bit matrix or a boolean matrix
35. This algorithm is used to find the shortest
paths in G when G is weighted.
First we define m-square boolean matrices
P0,P1…….,Pm as follows:
Let Pk[i,j] denote the i, j entry of the
matrix Pk.
36. Then we define
Pk[i,j]=
1 ,if there is a simple path from vi to vj which does
not use any other node except possibly v1,v2,
…..,vk
0 ,otherwise
In other words,
P0[i,j]=1, if there is an edge from vi to vj
P1[i,j]=1, if there is a simple path from vi to vj which does not use any other node
except possibly v1.
P2[i,j]=1, if there is a simple path from vi to vj which does not use any other node
except possibly v1 and v2.
37. Observe that P0=A, the adjacency matrix of G.
Since G has only m nodes, the last matrix
Pm=P, the path matrix of G.
Warshall observed that Pk[i,j]=1 can occur only
if one of the following two cases occur:
1. There is a simple path from vi to vj which does
not use any other node except possibly v1,v2,
….,vk-1, hence
Pk-1[i,j]=1
vi ……….. vj
38. 2. There is a simple path from vi to vk and
a simple path from vk to vj where each
path does not use any other nodes
except possibly v1,v2,……,vk-1; hence
Pk-1[i,k]=1 and Pk-1[k,j]=1
vi ……….. ………..vk vj
39. Accordingly, the elements of matrix Pk can
be obtained by:
Pk[i,j]=Pk-1[i,j] v (Pk-1[i,k] Pk-1[k,j])
Where V and denote the logical OR and AND operator
^ 0 1
0 0 0
1 0 1
v 0 1
0 0 1
1 1 1
42. Shortest path algorithm
Let G be a directed graph with m nodes
v1,v2,…..vm.
Suppose G is weighted i.e. suppose each
edge e in G is assigned a non negative
number w(e) called the weight or length of
the edge e.
43. Then G is maintained in memory by its
weight matrix W=(wij) defined as:
Wij= W(e), if there is an edge e from vi to vj
0, if there is no edge from vi to vj
44. Path matrix P tells us whether or not there are paths
between the nodes.
Now we want to find a matrix Q which tell us the lengths of
the shortest paths between the nodes or a matrix Q=(qij)
where
qij= length of shortest path from vi to vj.
Here we define a sequence of matrices Q1,Q2,…..Qm
defined as
Qk[i,j]= the smaller of the length of the preceding paths from
vi to vj or the sum of the lengths of preceeding paths from vi
to vk and from vk to vj.
Qk[i,j]=min(Qk-1[i,j],Qk-1[i,k]+Qk-1[k,j])
45. R U
S T
W =
R S T U
R
S
T
U
4
1275
3
7 5 0 0
7
7 0 0 2
0 3 0 0
4 0 1 0
46. R S T U
R
S
T
U
7 5 0 0
7 0 0 2
0 3 0 0
4 0 1 0
W =Q0 =