02 有向图和无向图

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1.Peer-to-Peer and Social Networks Fall 2017 Random Graphs

2.Directed vs. Undirected graphs Collaborations, Friendship on Facebook (Symmetric relationship) Emails, citations, Following on Twitter (asymmetric relationship)

3.Directed vs. Undirected graphs Degree of A = 4 In directed graphs, we have to define in-degree and out-degree of nodes. What is the average in-degree of the directed graph to the right? Degree of a node = number of edges incident on it . The average degree of nodes in an n-node graph G = (V, E) is

4.Sparse and dense graphs In a clique with n nodes, the degree of each node is ( n-1) . In dense graphs, the average node degree is O(n ) . In sparse graphs, the average degree is much smaller. Are most real world social networks dense or sparse? A clique

5.Real-world graphs Most real world graphs are sparse , i.e. the average degree is much less than n-1 for an n-node graph. Some examples are LinkedIn N = 6,946,668 Average degree = 8.87 Co-authorship (DBLP) = 317,080 Average degree = 6.62 Graphs can be weighted or un-weighted . Is the co-authorship network weighted or un-weighted ?

6.Random graphs Erdös-Renyi model One of several models of real world networks Presents a theory of how social webs are formed. Start with a set of isolated nodes Connect each pair of nodes with a probability The resulting graph is known as

7.Random graphs ER model is different from the model The model randomly selects one from the entire family of graphs with nodes and edges.

8.Properties of ER graphs Property 1 . The expected number of edges is Property 2 . The expected degree per node is Property 3 . The expected diameter of is [deg = expected degree of a node] [Note the word expected ] Why?

9.Degree distribution in random graphs Probability that a node connects with a given set of nodes (and not to the remaining remaining nodes) is One can choose out of the remaining nodes in ways. So the probability distribution is ( For large and small it is equivalent to Poisson distribution ) ( binomial distribution )

10.Degree distribution P(k ) = fraction of nodes that has degree k What distribution is this?

11.Important network properties Here path length means average path length between pairs of nodes

12.Clustering coefficient For a given node, its local clustering coefficient (CC) measures what fraction of its various pairs of neighbors are neighbors of each other. CC(B) = 3/6 = ½ CC(D) = 2/3 = CC(E) The global CC is the average of the local CC values. B’s neighbors are {A,C,D,E}. Only (A,D), (D,E), (E,C) are connected CC of a graph is the mean of the CC of its various nodes

13.Properties of ER graphs -- When , an ER graph is a collection of disjoint trees. -- When suddenly one giant (connected) component emerges. Other components have a much smaller size [ Phase change ]

14.Properties of ER graphs When the graph is almost always connected why? (i.e. connected with high probability ) Hints. The probability that a single node remains disconnected is (1-p)^(n-1). Substitute p = c.logn /n The clustering coefficient of an ER graph = p ( why ?} But a social network is not necessarily an ER graph! Human society is a “clustered” society, but ER graphs have poor (i.e. very low) clustering coefficient.

15.How social are you? Malcom Gladwell , a staff writer at the New Yorker magazine describes in his book The Tipping Point, an experiment to measure how social a person is. He started with a list of 248 last names A person scores a point if he or she knows someone with a last name from this list. If he/she knows three persons with the same last name, then he/she scores 3 points

16.How social are you? (Outcome of the Tipping Point experiment) Altogether 400 people from different groups were tested. (min) 9, (max) 118 {from a random sample} (min) 16, (max) 108 {from a highly homogeneous group} (min) 2, (max) 95 {from a college class} [Conclusion: Some people are very social, even in small or homogeneous samples. They are connectors ]

17.Connectors Barabási observed that connectors are not unique to human society only, but true for many complex networks ranging from biology to computer science , where there are some nodes with an anomalously large number of links . Certainly these types of clustering cannot be expected in ER graphs. The world wide web, the ultimate forum of democracy , is not a random network, as Barabási’s web-mapping project revealed.

18.Anatomy of the web Barabási first experimented with the Univ. of Notre Dame’s web. 325,000 pages 270,000 pages (i.e. 82%) had three or fewer links 42 had 1000+ incoming links each. The entire WWW exhibited even more disparity . 90% had ≤ 10 links , whereas a few (4-5) like Yahoo were referenced by close to a million pages! These are the hubs of the web . They help create short paths between nodes ( mean distance = 19 for WWW obtained via extrapolation ). ( Some dispute this figure now)

19.Random vs. Power-law Graphs The degree distribution in of the webpages in the World Wide Web follows a power-law

20.Random vs. Power-law Graphs

21.Random vs. Power-Law networks

22.Evolution of Scale -free networks

23.Example: Airline Routes Think of how new routes are added to an existing network

24.Preferential attachment New node Existing network A new node connects with an existing node with a probability proportional to its degree . The sum of the node degrees = 8 This leads to a power-law distribution ( Barabási & Albert) Also known as “ Rich gets richer ” policy

25.Anatomy of the web Albert, Jeong , B arabasi : Diameter of the World Wide Web. (Brief Communication). Nature 401, 9 Sep 1999

26.The web is a bow tie Reference: Nature 405, 113(11 May 2000)

27.Power law graph The degree distribution in of the webpages in the World Wide Web follow a power-law . In a power-law graph , the number of nodes with degree satisfies the condition Also known as scale-free graph . Other examples are -- Income and number of people with that income -- Magnitude and number of earthquakes of that magnitude -- Population and number of cities with that population