Dynamics of Real-world Networks
Description
In our recent work we found interesting and unintuitive patterns for
time evolving networks, which change some of the basic assumptions that
were made in the past. The main objective of observing the evolution
patterns is to develop models that explain processes which govern the
network evolution. Such models can then be fitted to real networks, and
used to generate realistic graphs or give formal explanations about
their properties. In addition, our work has a wide range of
applications: we can spot anomalous graphs and outliers, design better
graph sampling algorithms, forecast future graph structure and run
simulations of network evolution.
Another important aspect of this research is the study of ``local''
patterns and structures of propagation in networks. We aim to identify
building blocks of the networks and find the patterns of influence that
these block have on information or virus propagation over the network.
Our recent work included the study of the spread of influence in a large
person-to-person product recommendation network and its effect on
purchases. We also model the propagation of information on the
blogosphere, and propose algorithms to efficiently find influential
nodes in the network.
Further work will include three areas of research. We will continue
investigating models for graph generation and evolution. Second, we will
analyze large online communication networks and devise models on how
user characteristics and geography relate to communication and network
patterns. Third, we will extend the work on the propagation of influence
in recommendation networks to blogs on the Web, studying how information
spreads over the Web by finding influential blogs and analyzing their
patterns of influence.
http://www.cs.cmu.edu/~jure/thesis
Thesis Committee:
Christos Faloutsos (Chair)
Avrim Blum
John Kleinberg (Cornell University)
John Lafferty
| Slides | |
| 0:00 | - Dynamics of Real-world Networks - Answers |
| 0:40 | Committee members |
| 0:51 | Network dynamics |
| 1:42 | Large real world networks |
| 2:19 | Questions we ask |
| 2:51 | Our work: Network dynamics |
| 3:11 | Our work: Goals |
| 3:42 | Our work: Overview - Page 1 |
| 4:08 | Our work: Overview - Page 2 |
| 4:18 | Our work: Impact and applications |
| 5:50 | Outline - Page 1 |
| 6:32 | Completed work: Overview - Page 1 |
| 6:49 | Completed work: Overview - Page 2 |
| 7:00 | G1 - Patterns: Densification |
| 9:01 | G1 - Patterns: Shrinking diameters |
| 9:51 | G2 - Models: Kronecker graphs |
| 10:58 | Idea: Recursive graph generation |
| 11:56 | Kronecker product: Graph - Page 1 |
| 12:33 | Kronecker product: Graph - Page 2 |
| 13:13 | Properties of Kronecker graphs |
| 14:23 | G3 - Predictions: The problem |
| 15:11 | Model estimation: approach |
| 16:01 | Model estimation: solution |
| 17:23 | Model estimation: experiments - Page 1 |
| 18:04 | Model estimation: experiments - Page 2 |
| 18:30 | Completed work: Overview |
| 21:08 | Information cascades |
| 22:09 | Cascades: Questions |
| 22:49 | Cascades in viral marketing |
| 24:00 | Product recommendation network |
| 24:56 | G1- Viral cascade shapes |
| 25:46 | G1- Viral cascade sizes |
| 26:40 | Does receiving more recommendations increase the likelihood of buying? |
| 27:28 | Cascades in the blogosphere |
| 28:39 | G1- Blog cascade shapes |
| 29:19 | G1- Blog cascade size |
| 29:49 | G2- Blog cascades: model |
| 30:36 | G3- Node selection for cascade detection |
| 31:40 | Node selection: algorithm |
| 32:14 | Outline - Page 2 |
| 32:32 | Proposed work: Overview |
| 33:12 | Proposed work: Communication networks - Page 1 |
| 33:56 | Proposed work: Communication networks - Page 2 |
| 34:41 | Proposed work: Links & cascades |
| 36:05 | Proposed work: Kronecker graphs |
| 37:06 | Timeline |
| 37:37 | References |
| 37:52 | - Dynamics of Real-world Networks - Questions |
| 39:42 | - G1- Blog cascade shapes - Answers |
| 50:16 | - Kronecker product: Graph - Page 1 - Answers |
| 58:00 | - Proposed work: Kronecker graphs - Answers |
| 67:59 | - The model: Forest Fire Model - Answers |
| 69:05 | - Forest Fire Model 2 - Answers |
| 69:20 | - Properties of the Forest Fire - Answers |
| 70:42 | - Dynamics of Real-world Networks - Answers |
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