Recommending Links to Maximize the Influence in Social Networks
Authors: Federico Corò, Gianlorenzo D'Angelo, Yllka Velaj
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally show that, with few new links and small computational time, our algorithm is able to increase by far the social influence of the target users. We compare our algorithm with several baselines and show that it is the most effective one in terms of increased influence. |
| Researcher Affiliation | Academia | 1Gran Sasso Science Institute, L Aquila, Italy 2CWI, Amsterdam, Netherlands 3ISI Foundation, Turin, Italy {federico.coro, gianlorenzo.dangelo}@gssi.it, yllka.velaj@{cwi.nl, isi.it} |
| Pseudocode | Yes | Algorithm 1 Greedy algorithm for IMA. |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing the code or links to a code repository for the methodology described. |
| Open Datasets | Yes | We evaluate the performance of the algorithm on four types of randomly generated directed networks which exhibit many of the structural features of complex networks and on real-world graphs that are suitable for our problem, taken from KONECT [Kunegis, 2013], Arnet Miner [Arnetminer, 2015] and SNAP repositories. |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits. It mentions choosing 0.1% of nodes as seeds and adding a fixed number of edges. |
| Hardware Specification | Yes | All our experiments have been performed on a computer equipped with two Intel Xeon E5-2643 CPUs (6 cores clocked at 3.4GHz) and 128GB RAM; our programs have been implemented in C++ (gcc compiler v4.8.2 with optimization level O3). |
| Software Dependencies | Yes | our programs have been implemented in C++ (gcc compiler v4.8.2 with optimization level O3). |
| Experiment Setup | Yes | For both synthetic and real-world networks, we choose 0.1% of the nodes in V as seeds and we add up to B = 2 |A| edges. For these experiments, the seed nodes are chosen uniformly at random. The weights on the edges in both models are generated as follows: In ICM we are assigned the probabilities to the edges according to the weighted model, i.e., for each edge (u, v), assign wuv = 1/Nv; In LTM instead we generate for each node v V a random variable wv [0, 0.5] that represent the probability that v does not select any edge in the live-edge graph, then we assigned for each edge (u, v) in the graph a weight equal to 1 wv Nv and wv 2 is assigned to a new edge. As a measure of the quality of the solution, we adopt the expected number of active nodes σ(A, S). As discussed in the preliminaries, it has been proven that evaluating this function is #P-complete in general. However, by simulating the diffusion process a polynomial number of times and sampling the resulting active sets, it is possible to obtain arbitrarily good approximations to σ(A, S). We experimentally tested that 500 samples are enough to obtain a good estima-tion. Hence, we run 500 trial to estimate the value of σ in the algorithms and in the final solution. |