Viral Marketing and Convergence Properties in Generalised Voter Model
Authors: Abhiram Manohara, Ahad N. Zehmakan
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on realworld and synthetic graph data demonstrate that the proposed algorithm outperforms other algorithms. We also prove that the process could take an exponential number of rounds to converge. However, if we limit ourselves to strongly connected graphs, the convergence time is polynomial and the convergence period (size of the stationary configuration) is bounded by the highest common divisor of cycle lengths in the network. |
| Researcher Affiliation | Academia | 1 Indian Institute of Science, Bangalore, India 2 School of Computing, The Australian National University, Canberra, Australia EMAIL, EMAIL |
| Pseudocode | No | The paper describes a "greedy algorithm" in Section 4.2 but presents its steps in prose rather than a structured pseudocode or algorithm block. For example, it states: "The greedy algorithm works by iteratively selecting and adding the node which gives the highest increase to the expected number of blue nodes in time τ." |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code, nor does it provide any links to a code repository or mention code in supplementary materials for the methodology described. |
| Open Datasets | Yes | We have used real-world social network data available on SNAP database [Leskovec and Krevl, 2014], including Facebook 0 (n = 347, m = 5, 038), Twitter (n = 475, m = 13, 289), Facebook 414 (n = 685, m = 3, 386), Wikipedia (n = 4, 592, m = 119, 882), Bitcoin OTC (n = 6, 005, m = 35, 592), Gnutella (n = 6, 300, m = 20, 777), and Bitcoin Alpha (n = 7, 604, m = 24, 186). |
| Dataset Splits | No | The paper lists several real-world social network datasets used for experiments but does not provide specific information regarding how these datasets were split (e.g., into training, validation, or test sets). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to conduct the experiments. |
| Software Dependencies | No | The paper does not specify any particular software, libraries, or solvers with their corresponding version numbers used in the experimental setup. |
| Experiment Setup | Yes | Each run has 20 red nodes and a budget varying from 1 to 40 for τ = 20 rounds. |