Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.
-
date post
19-Dec-2015 -
Category
Documents
-
view
215 -
download
0
Transcript of Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.
![Page 1: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/1.jpg)
Mining and Searching Massive Graphs (Networks)
Introduction and BackgroundLecture 1
![Page 2: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/2.jpg)
Welcome!
Instructor: Ruoming Jin Homepage: www.cs.kent.edu/~jin/ Office: 264 MCS Building Email: [email protected] Office hour: Tuesdays and Thursdays
(10:30AM to 11:00AM) or by appointment
![Page 3: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/3.jpg)
Overview
Homepage: www.cs.kent.edu/~jin/graph mining.html
Time: 11:00-12:15PM Tuesdays and Thursdays
Place: MSB 276 Prerequisite:
Preferred: Machine Learning, Algorithms, and Data Structures
Preferred Math: Probability, Linear Algebra, Graph Theory
![Page 4: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/4.jpg)
Course overview
The course goal First of all, this is a research course, or a
special topic course. There is no textbook and even no definition what topics are supposed to under the course name
But we will try to understand the underlying (math and cs) techniques!
Learning by reading and presenting some recent interesting papers!
Thinking about interesting ideas (don’t forget this is a research course!)
![Page 5: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/5.jpg)
Topics
1. Basic Math for Massive Graph Mining Probability (Classical Random Graph Theory) Linear Algebra (Eigenvalue/Eigenvector, SVD) Markov Chain and Random Walk
2. Statistical Properties of Real Networks3. Models for Networks4. Process and Dynamics on the Network
![Page 6: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/6.jpg)
Requirement
Each of you will have two presentations Project
Select a topic and review at least four papers Develop a new idea on this topic Surplus: do an implementation exam Final term paper on the survey topic, your idea
and possible implementation/evaluation
Final Grade: 50% presentations, 40% project, and 10% class participation
![Page 7: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/7.jpg)
What is a network?
Network: a collection of entities that are interconnected with links. people that are friends computers that are interconnected web pages that point to each other proteins that interact
![Page 8: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/8.jpg)
Graphs
In mathematics, networks are called graphs, the entities are nodes, and the links are edges
Graph theory starts in the 18th century, with Leonhard Euler The problem of Königsberg bridges Since then graphs have been studied
extensively.
![Page 9: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/9.jpg)
Networks in the past
Graphs have been used in the past to model existing networks (e.g., networks of highways, social networks) usually these networks were small network can be studied visual inspection
can reveal a lot of information
![Page 10: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/10.jpg)
Networks now
More and larger networks appear Products of technological advancement
• e.g., Internet, Web
Result of our ability to collect more, better, and more complex data• e.g., gene regulatory networks
Networks of thousands, millions, or billions of nodes impossible to visualize
![Page 11: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/11.jpg)
The internet map
![Page 12: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/12.jpg)
Understanding large graphs
What are the statistics of real life networks?
Can we explain how the networks were generated?
What else? A still very young field! (What is the basic principles and
what those principle will mean?)
![Page 13: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/13.jpg)
Measuring network properties
Around 1999 Watts and Strogatz, Dynamics and
small-world phenomenon Faloutsos3, On power-law relationships
of the Internet Topology Kleinberg et al., The Web as a graph Barabasi and Albert, The emergence of
scaling in real networks
![Page 14: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/14.jpg)
Real network properties
Most nodes have only a small number of neighbors (degree), but there are some nodes with very high degree (power-law degree distribution) scale-free networks
If a node x is connected to y and z, then y and z are likely to be connected high clustering coefficient
Most nodes are just a few edges away on average. small world networks
Networks from very diverse areas (from internet to biological networks) have similar properties Is it possible that there is a unifying underlying generative
process?
![Page 15: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/15.jpg)
Generating random graphs
Classic graph theory model (Erdös-Renyi) each edge is generated independently with
probability p
Very well studied model but: most vertices have about the same degree the probability of two nodes being linked is
independent of whether they share a neighbor the average paths are short
![Page 16: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/16.jpg)
Modeling real networks
Real life networks are not “random” Can we define a model that
generates graphs with statistical properties similar to those in real life? a flurry of models for random graphs
![Page 17: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/17.jpg)
Processes on networks
Why is it important to understand the structure of networks?
Epidemiology: Viruses propagate much faster in scale-free networks
Vaccination of random nodes does not work, but targeted vaccination is very effective
![Page 18: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/18.jpg)
The future of networks
Networks seem to be here to stay More and more systems are modeled as
networks Scientists from various disciplines are
working on networks (physicists, computer scientists, mathematicians, biologists, sociologist, economists)
There are many questions to understand.
![Page 19: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/19.jpg)
Basic Mathematical Tools
Graph theory Probability theory Linear Algebra
![Page 20: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/20.jpg)
Graph Theory
Graph G=(V,E) V = set of vertices E = set of edges
1
2
3
45
undirected graphE={(1,2),(1,3),(2,3),(3,4),(4,5)}
![Page 21: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/21.jpg)
Graph Theory
Graph G=(V,E) V = set of vertices E = set of edges
1
2
3
45
directed graphE={‹1,2›, ‹2,1› ‹1,3›, ‹3,2›, ‹3,4›, ‹4,5›}
![Page 22: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/22.jpg)
Undirected graph
1
2
3
45
degree d(i) of node i number of edges
incident on node i
degree sequence [d(1),d(2),d(3),d(4),d(5)] [2,2,2,1,1]
degree distribution [(1,2),(2,3)]
![Page 23: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/23.jpg)
Directed Graph
1
2
3
45
in-degree din(i) of node i number of edges pointing
to node i
out-degree dout(i) of node i number of edges leaving
node i in-degree sequence
[1,2,1,1,1]
out-degree sequence [2,1,2,1,0]
![Page 24: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/24.jpg)
Paths
Path from node i to node j: a sequence of edges (directed or undirected from node i to node j) path length: number of edges on the path nodes i and j are connected cycle: a path that starts and ends at the same node
1
2
3
45
1
2
3
45
![Page 25: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/25.jpg)
Shortest Paths
Shortest Path from node i to node j also known as BFS path, or geodesic
path
1
2
3
45
1
2
3
45
![Page 26: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/26.jpg)
Diameter
The longest shortest path in the graph
1
2
3
45
1
2
3
45
![Page 27: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/27.jpg)
Undirected graph
1
2
3
45
Connected graph: a graph where there every pair of nodes is connected
Disconnected graph: a graph that is not connected
Connected Components: subsets of vertices that are connected
![Page 28: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/28.jpg)
Fully Connected Graph
Clique Kn
A graph that has all possible n(n-1)/2 edges
1
2
3
45
![Page 29: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/29.jpg)
Directed Graph
1
2
3
45
Strongly connected graph: there exists a path from every i to every j
Weakly connected graph: If edges are made to be undirected the graph is connected
![Page 30: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/30.jpg)
Subgraphs
1
2
3
45
Subgraph: Given V’ V, and E’ E, the graph G’=(V’,E’) is a subgraph of G.
Induced subgraph: Given V’ V, let E’ E is the set of all edges between the nodes in V’. The graph G’=(V’,E’), is an induced subgraph of G
![Page 31: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/31.jpg)
Trees
Connected Undirected graphs without cycles
1
2
3
45
![Page 32: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/32.jpg)
Bipartite graphs
Graphs where the set V can be partitioned into two sets L and R, such that all edges are between nodes in L and R, and there is no edge within L or R
![Page 33: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/33.jpg)
Linear Algebra
Adjacency Matrix symmetric matrix for undirected graphs
1
2
3
45
01000
10100
01011
00101
00110
A
![Page 34: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/34.jpg)
Linear Algebra
Adjacency Matrix unsymmetric matrix for undirected
graphs
00000
10000
01010
00001
00110
A 1
2
3
45
![Page 35: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/35.jpg)
Random Walks
Start from a node, and follow links uniformly at random.
Stationary distribution: The fraction of times that you visit node i, as the number of steps of the random walk approaches infinity if the graph is strongly connected, the
stationary distribution converges to a unique vector.
![Page 36: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/36.jpg)
Random Walks
stationary distribution: principal left eigenvector of the normalized adjacency matrix x = xP for undirected graphs, the degree distribution
1
2
3
45
00001
10000
0210210
00001
0021210
P
![Page 37: Mining and Searching Massive Graphs (Networks) Introduction and Background Lecture 1.](https://reader030.fdocuments.in/reader030/viewer/2022032703/56649d265503460f949fcd0b/html5/thumbnails/37.jpg)
Eigenvalues and Eigenvectors
The value λ is an eigenvalue of matrix A if there exists a non-zero vector x, such that Ax=λx. Vector x is an eigenvector of matrix A The largest eigenvalue is called the
principal eigenvalue The corresponding eigenvector is the
principal eigenvector Corresponds to the direction of
maximum change