Influence Maximization (IM) on Social Networks: The State-of-the-Art and the Gaps that Remain

Influence maximization (IM) on social networks is one of the most active areas of research in computer science. While various IM techniques proposed over the last decade have definitely enriched the field, unfortunately, experimental reports on existing techniques fall short in validity and integrity since many comparisons are not based on a common platform or merely discussed in theory. In this paper, we perform an in-depth benchmarking study of IM techniques on social networks. Specifically, we design a benchmarking platform, which enables us to evaluate and compare the existing techniques systematically and thoroughly under identical experimental conditions. Our benchmarking results analyze and diagnose the inherent deficiencies of the existing approaches and surface the open challenges in IM even after a decade of research. More fundamentally, we unearth and debunk a series of myths and establish that there is no single state-of-the-art technique in IM. At best, a technique is the state of the art in only one aspect.

Related Publications (*Equal Contribution)
Debunking the Myths of Influence Maximization: An in-depth Benchmarking Study
Akhil Arora*, Sainyam Galhotra* and Sayan Ranu
Proc. of the 2017 ACM SIGMOD International Conference on Management of Data.

Source Code [Work in Progress]: Please see the im_benchmarking repository on github.
Note: It has come to our notice that groups in University of British Columbia (headed by Lakshmanan et al.) and Nanyang Technical University (Xiao et al.), have published refutations on our benchmarking study as a technical report. A point by point response to the refutations would be placed on our websites soon!
Additionally, we have the following comments to make on their refutations:
  • The authors of this technical report had earlier sent an email to the SIGMOD PC chair stating that our paper possess serious flaws. The refutations detailed in the technical report available at arXiv (version 3), are largely based on the set of flaws that they had pointed out in their email. We would like to bring this to the kind notice of the reader of this note and the technical report on refutations that our paper was discussed again by the SIGMOD PC Committee including the original reviewers of our paper, and was still marked to be fit and worthy for a place in the proceedings of SIGMOD 2017. This gives a reasonable level of assurance that our paper is of SIGMOD quality. [Email by Laks et al.] [Our Response] [Response by SIGMOD'17 PC Chair]
  • Furthermore, we took part in the SIGMOD 2017/2018 reproducibility exercise, where our experimental environment and results were evaluated rigorously by neutral third parties, and our paper has been marked SIGMOD Reproducible! Additionally, a more detailed version of our response to these refutations is now available.
Note: Due to an undocumented assumption in the SIMPATH code released by the authors of SIMPATH paper, the running times reported for SIMPATH on the DBLP and YouTube datasets in our paper is not a true reflection of the SIMPATH algorithm. We will release a technical report soon with the correct running times.

The IM Benchmarking Architecture

The IM Benchmarking Architecture

Systematic benchmarking platform consisting of the following four core components:
  • Setup, including a set of algorithms, real-world datasets, parameter configurations and a diffusion model.
  • IM Framework, a generalized IM module with high abstraction of the common workflow of Influence Maximization.
  • Evaluation, which provide targeted diagnoses on these algorithms based on our framework, leading to directions of improvement over the existing work.
  • Insights, which discusses the key take-away points from the benchmarking study and generally, summarizes the state of the IM field after more than a decade of research.

Techniques benchmarked
The 11 representative techniques evaluated in this study are : CELF, CELF++, TIM, IMM, PMC, StaticGreedy, LDAG , SIMPATH, EaSyIM, IRIE and IMRank.
All these techniques were benchamrked over the classical models of information diffusion, namely -- Independent Cascade (IC), Weighted Cascade (WC) and Linear Threshold (LT).

Evolution of techniques

Note: We are in the process of including Stop-and-Stare into our benchmarking evaluation and are currently running experiments using the code provided by the authors.

Categorization of IM techniques

The vast literature of IM techniques can be categorized as follows:

Categorization of IM techniques

Need for benchmarking

  • Ambiguities: IC vs WC
    • Existing Literature: Use IC/WC interchangeably
    • Actual Scenario: Varied behavior in terms of the spread of seed nodes, efficiency and scalability aspects of different techniques
    IC vs. WC

  • Evaluation metrics: State-of-the-art technique with respect to one metric performs the worst in other aspect of the problem.

  • Comparison of time Comparison of memory

Useful Insights

  • One Size Doesn't Fit All! No technique performs the best over all aspects of IM. The venn-diagram summarizes the different aspects that are optimized by the various techniques.

  • Qualitative categorization of IM techniques

  • Which technique to choose and when? The decision tree presents the best IM technique given the task and resources.

  • Which technique to choose and when?

Primary Contributors

Additional Contributors

  • Suqi Cheng