Deanonymizing Social Networks Using Structural Information
Authors: Ioannis Caragiannis, Evanthia Tsitsoka
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that by combining the two algorithms, we can tolerate noise at the level of up to 10% and deanonymize correctly large fractions of the network nodes. |
| Researcher Affiliation | Academia | Ioannis Caragiannis and Evanthia Tsitsoka University of Patras, Greece {caragian,tsitsoka}@ceid.upatras.gr |
| Pseudocode | Yes | Algorithm 1 The local search algorithm |
| Open Source Code | No | The paper states 'We have implemented in C both the NDSD and the local search algorithm and have used them in all our experiments.' but does not provide a link or explicit statement about public code availability. |
| Open Datasets | Yes | The real-world instances include the ego-facebook graph from the SNAP collection of social networks [Leskovec and Krevl, 2014] as well as sixteen facebook graphs from the Network Repository [Rossi and Ahmed, 2015]. |
| Dataset Splits | No | The paper describes generating noisy graphs (H) from original graphs (G) using parameters δ and ϵ to simulate real-world conditions for testing, but it does not specify traditional train/validation/test dataset splits, percentages, or cross-validation strategies needed for reproducibility of data partitioning. |
| Hardware Specification | Yes | Our computational results (including what we report here as well as additional ones that we omit due to lack of space) have been obtained using a desktop PC with an Intel i7-4790/3.6GHz processor with 8GB of RAM, running a Slackware64 operating system. |
| Software Dependencies | No | The paper mentions implementation in C, use of NetworkX, and 'running a Slackware64 operating system', but it does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | In particular, we build H from G using two parameters, δ and ϵ. The parameter δ indicates the fraction of nodes that are removed from graph G together with their incident edges. Next, we remove an ϵ fraction of the edges that survived and replace each of them by a random (new) edge in the graph. In our experiments with real-world data from the Network Repository, we have used different values between 0 and 20% for δ and between 0 and 10% for ϵ. |