By My T. Thai, Nam P. Nguyen, Huawei Shen

ISBN-10: 3319217852

ISBN-13: 9783319217857

ISBN-10: 3319217860

ISBN-13: 9783319217864

This e-book constitutes the refereed lawsuits of the 4th foreign convention on Computational Social Networks, CSoNet 2015, held in Beijing, China, in August 2015.

The 23 revised complete papers and three brief papers offered including 2 prolonged abstracts have been rigorously reviewed and chosen from a hundred and one submissions and canopy themes on social details diffusion; community clustering and neighborhood constitution; social hyperlink prediction and advice; and social community constitution analysis.

**Read Online or Download Computational Social Networks: 4th International Conference, CSoNet 2015, Beijing, China, August 4-6, 2015, Proceedings PDF**

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**Additional info for Computational Social Networks: 4th International Conference, CSoNet 2015, Beijing, China, August 4-6, 2015, Proceedings**

**Sample text**

Output: Sequence p of prices. 1. G0 ← G, t ← 0 2. While V (Gt ) = ∅ do 3. t←t+1 4. pt ← max{ν(i) + dw Gt−1 (i) : i ∈ V (Gt−1 )} 5. Gt ← Gt−1 \ B(pt ) 6. End-while 7. Output p ← (p1 , p2 , . . 2 Optimal Pricing for Unweighted Split Networks Network G = (V, E) is a split network if its node set V can be partitioned into two sets C and I such that C induces a clique and I is an independent set of G. Clearly, the nodes in I can only have neighbors in C. In case of each node in I adjacent to exactly one node in C, network G is called core-peripheral.

Near Optimal Pricing for Erd˝ os-R´ enyi Networks For large n, there is a simple algorithm that is “almost optimal” for “almost all” Erd˝ os-R´enyi networks G(n, η(n)). The network is constructed by connecting n nodes randomly; each link is included in the network with probability η(n). This algorithm, which will be referred to as A(δ), prices only once with price (1 − δ)(n−1)η(n), where δ > 0 is a parameter to be determined by the approximation ratio that we intend to reach. Theorem 5. Given arbitrarily small positive number > 0, set δ ∈ (0, 1) such 1+δ < 1+ .

MinGreedy: The greedy algorithm targets on minimizing misinformation propagation; the node with maximum number of A-active nodes blocked is selected in each iteration [2,9]. 2 Experimental Results In this part, we ﬁrst measure the performance of our algorithm, in which we evaluate the number of A-active nodes and B-active nodes as well as their diﬀerence across three real world datasets with diﬀerent number of seeds and rounds. Secondly, we compare the the results from all above mentioned algorithms.

### Computational Social Networks: 4th International Conference, CSoNet 2015, Beijing, China, August 4-6, 2015, Proceedings by My T. Thai, Nam P. Nguyen, Huawei Shen

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