Oz Shaharabani. Study topic Detailed study of network evolution by analyzing four large online...
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Microscopic Evolution of Social Networks Oz Shaharabani
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Transcript of Oz Shaharabani. Study topic Detailed study of network evolution by analyzing four large online...
- Slide 1
- Oz Shaharabani
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- Study topic Detailed study of network evolution by analyzing four large online social networks with full temporal information about node and edge arrivals.
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- What is the goal? To develop a complete model of network evolution which accurately reflects the true network in all four cases.
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- Study approach the microscopic behavior of nodes solely determines the macroscopic network properties
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- Model core processes 1. Node arrival process - governs the arrival of new nodes into the network. 2. Edge initiation process - determines for each node when it will initiate a new edge. 3. Edge destination selection process -determines the destination of a newly initiated edge.
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- Datasets
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- Notations
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- Preferential Attachment
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- 1. Edge attachment by degree. 2. Edges attachment by the age of the node. 3. Bias towards node age and degree.
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- Preferential Attachment 1. Edge attachment by degree. 2. Edges attachment by the age of the node. 3. Bias towards node age and degree.
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- Edge attachment by degree
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- Back to our networks:
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- Edge attachment by degree Conclusion:
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- Preferential Attachment 1. Edge attachment by degree. 2. Edges attachment by the age of the node. 3. Bias towards node age and degree.
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- Edge attachment by nodes age
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- We define e(a) to be the average number of edges created by nodes of age a.
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- Edge attachment by nodes age We define e(a) to be the average number of edges created by nodes of age a.
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- Preferential Attachment 1. Edge attachment by degree. 2. Edges attachment by the age of the node. 3. Bias towards node age and degree.
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- Maximum-likelihood principle - Maximum-likelihood principle . , , " " .
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- Maximum-likelihood principle
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- Bias towards node age and degree We will see four models for choosing the edge endpoints at time t. (Using the MLE principle).
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- Bias towards node age and degree
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- We conclude that PA (model D) performs reasonably well compared to more sophisticated variants based on degree and age. i.e.,the probability of selecting a node v is.proportional to its current degree
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- Locality of edge attachment
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- Notation: Edge locality of edge (u,v), its the number of hopes its span. i.e., the length of the shortest path between nodes u and w immediately before the edge was created.
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- Locality of edge attachment
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- Here the distributions of these shortest path values induced by each new edge for the four networks.
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- Locality of edge attachment
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- What is the conclusion?
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- Locality of edge attachment Conclusion: Most of the are most likely to close triangles, i.e., connect people with common friends.
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- Triangle-closing models Given that such a high fraction of edges close triangles, we aim to model how a length-two path should be selected. We will see five models of choosing neighborhood node.
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- Triangle-closing models
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- We will focus on random-random model because: Gives higher probability to nodes with more length-two paths. (therefore, its biased towards high-degree nodes). Gives a sizable chunk of the performance gain over the baseline (10%). Much simple then the other models.
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- Node and edge arrival process
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- We want to create an optimal model, but we have to answer some questions before: Which nodes initiate edges? How long a node remains active in the social network? What are the specific times at which the node initiates new edges?
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- Node and edge arrival process
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- Node arrivals
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- The final network evolution model
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- We now show that our model, node lifetime combined with gaps, produces power law out-degree distribution. Why we want to produces power law out-degree distribution?
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- The final network evolution model Why we want to produces power law out-degree distribution? Its very important property of social network! nodes degree
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- The final network evolution model
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- Proof: (at home)
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- Validation of the model
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- Result (on FLICKER for example):
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- Validation of the model Result (on FLICKER for example):
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- Conclusions
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