InfluenceMaximizationBenchmarker

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InfluenceMaximizationBenchmarker

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Implementation of the Influence Maximization Benchmarker (IMB)

InfluenceMaximizationBenchmarker 최신버젼 다운로드

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# InfluenceMaximizationBenchmarker

The InfluenceMaximizationBenchmarker (IMB) is a benchmark suite first published at GRADES-NDA 2021:

> Heiko Geppert, Sukanya Bhowmik, and Kurt Rothermel. 2021. Large-scale Influence Maximization with the Influence Maximization Benchmarker Suite. In 4th Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) (GRADES-NDA’21), June 20–25, 2021, Virtual Event, China. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3461837.3464510

IMB provides easy to use implementation of the Influence Maximization (IM) problem.

## Building

### Dependencies

*  cmake (3.24 or higher)
*  clang (version?) or GCC (11.1 or higher)

### How to build

```
mkdir build
cd build
cmake -G "Unix Makefiles" -DBUILD_TESTS=<1|0> -DCMAKE_BUILD_TYPE=<Release|Debug> -DUSE_CLANG=<1|0> ..
make -j<number of cores>
```

## Running

Running Degree Discount and Update Approximation (UA) with a hop distance of 3 on the epinions vertex list.

`./InfluenceMaximizationBenchmarker --graph ./data/epinions.txt --format 0 --algorithm degree,ua`

### Parameters
| short parameter | long parameter | type | description |
|---|---|---|---|
|-g | --graph | string | path to the graph file |
|-f| --format | int | format of the graph file. 0 = vertex list, 1 = edge list. Default=0 |
|-o | --output | string | path to an (optional) output file, where the result set is written |
|-s | --simulations | int | number of influence propagations simulations that are performed to evaluate the result set (only affects the evaluation). Default=20,000|
|-t | --threads | int | number of threads to be used (affects only some algrithms). Default=#CPU-cores|
|-k | --seeds | int | number of seed nodes to be searched (result set size) |
|-a | --algorithm | string | algorithms to be used. Multiple possible (comma separated). See table below for possible options.|
|-r | --raw | None | Flag to produce non pretty (raw) outputs. Great for machine parsing. |
|-i | --inverse | None | Flag, if set, the edge directions will be inversed. |
|-e | --random-edge-weights | None | Flag, if set every the weight of every edge will be one of {0.1, 0.01, 0.001} choosen randomly at the beginning. |

#### Algorithms
| original name | parameter name | arguments | 
|---|---|---|
| PageRank | pagerank | int: iterations, if none is given PR is performed until it converges (0.0001) |
| High Degree | highdegree | none |
| Degree Discount | degree | none |
| Random | random | none |
| CELF-Greedy | celf | int: number of MC simulations per vertex calculation, Default=10,000 |
| Influence Path Algorithm (IPA) | ipa | int: divider of the IPA threshold, Default=320 |
| Weighted Degree | wd | none |
| EaSyIM | easyim | int: hop distance, Default=3|
| EaSyIM-Delta | easyim-delta | int: hop distance, Default=3 |
| Update Approximation | ua | int: hop distance, Default=3 |
| IMM | imm | none |

Note: IMM is not sufficiently tested yet and should be used with caution.

#### original papers:
**CELF-Greedy:**
Jure Leskovec, Andreas Krause, Carlos Guestrin, Christos Faloutsos, Jeanne VanBriesen, and Natalie Glance. 2007. Cost-effective outbreak detection in networks. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '07). Association for Computing Machinery, New York, NY, USA, 420–429. DOI:https://doi.org/10.1145/1281192.1281239

**Influence Path Algorithm (IPA):**
J. Kim, S. Kim and H. Yu, "Scalable and parallelizable processing of influence maximization for large-scale social networks?," 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013, pp. 266-277, doi: 10.1109/ICDE.2013.6544831.

**EaSyIM:** 
Sainyam Galhotra, Akhil Arora, and Shourya Roy. 2016. Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models. In Proceedings of the 2016 International Conference on Management of Data (SIGMOD '16). Association for Computing Machinery, New York, NY, USA, 743–758. DOI:https://doi.org/10.1145/2882903.2882929

**Update Approximation:** Heiko Geppert, Sukanya Bhowmik, and Kurt Rothermel. 2021. Large-scale Influence Maximization with the Influence Maximization Benchmarker Suite. In 4th Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) (GRADES-NDA’21), June 20–25, 2021, Virtual Event, China. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3461837.3464510

**IMM:** 
Youze Tang, Yanchen Shi, and Xiaokui Xiao. 2015. Influence Maximization in Near-Linear Time: A Martingale Approach. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (SIGMOD '15). Association for Computing Machinery, New York, NY, USA, 1539–1554. DOI:https://doi.org/10.1145/2723372.2723734

## Input Format

The input graphs can be provided in two formats: vertex lists and edge lists.
Examples can be found in the 'data' directory.

For details see [INPUT DOC](./doc/input.md).

# InfluenceMaximizationEvaluator

The InfluenceMaximizationEvaluator is a little extra tool to run the influence diffusion process on a given graph, using a given seed set.
Hence, the influence maximization strategy is skipped.
This can be used, if you want to verify or calculate the influence of a third party IM strategy using our diffusion implementation.

## Building

The InfluenceMaximizationEvaluator is build when the InfluenceMaximizationBenchmarker is build.
No additional cmmands needed.

## Running

`./InfluenceMaximizationEvaluator --graph ../data/epinions.txt --format 0 -s 10000 --algorithm ua -p ../data/epinions_seed_set.txt`

The parameters are mostly equal to the InfluenceMaximizationBenchmarker.
Note the following new arguments:

| short parameter | long parameter | type | description |
|---|---|---|---|
|-p | --seedSetPath | string | path to the seed set file |
|-m | --metaData | None | Flag, if set the first line in the graph file will be skipped. Use if the first line (without escape signs like '#' or '%' holds the number of nodes and edges in the data set|

Also note, that the InfluenceMaximizationEvaluator uses the algorithm (-a) parameter only for documentation.
Hence, you can pass whatever name the algorihm that created the seed set had.

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