Optimizing Network Resilience via Vertex Anchoring
Authors: 
Siyi Teng, Jiadong Xie, Fan Zhang, Can Lu, Juntao Fang, Kai WangAuthors Info & Claims
Siyi Teng, Jiadong Xie, Fan Zhang, Can Lu, Juntao Fang, Kai WangAuthors Info & ClaimsPages 606 - 617
Abstract
Network resilience is a critical ability of a network to maintain its functionality against disturbances. A network is resilient/robust when a large portion of the nodes are to be better engaged in the network, i.e., they are less likely to leave given the changes on the network. Existing studies validate that the engagement of a node can be well captured by its coreness on network topology. Therefore, it is promising to maximize the number of nodes with increasing coreness values. In this paper, we propose and study thefollower maximization problem: maximizing the resilience gain (the number of coreness-increased vertices) via anchoring a set of vertices within a given budget. We prove that the problem is NP-hard and W[2]-hard, and it is NP-hard to approximate within an O(n^1-ε ) factor. We first propose an advanced greedy approach, followed by a time-dependent framework designed to quickly find high-quality results. The framework is initialized by the advanced greedy algorithm and incorporates novel techniques for optimizing the search space. The effectiveness and efficiency of our solution are verified with extensive experiments on 8 real-life datasets. Our source codes are available at https://github.com/Tsyxxxka/Follower-Maximization.
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References
[1]
Réka Albert, Hawoong Jeong, and Albert-László Barabási. 2000. Error and attack tolerance of complex networks. nature, Vol. 406, 6794 (2000), 378--382.
[2]
Vladimir Batagelj and Matjaz Zaversnik. 2003. An O (m) algorithm for cores decomposition of networks. arXiv preprint cs/0310049 (2003).
[3]
Katja Berdica. 2002. An introduction to road vulnerability: what has been done, is done and should be done. Transport policy, Vol. 9, 2 (2002), 117--127.
[4]
Alina Beygelzimer, Geoffrey Grinstein, Ralph Linsker, and Irina Rish. 2005. Improving network robustness by edge modification. Physica A: Statistical Mechanics and its Applications, Vol. 357, 3--4 (2005), 593--612.
[5]
Kshipra Bhawalkar, Jon M. Kleinberg, Kevin Lewi, Tim Roughgarden, and Aneesh Sharma. 2015. Preventing Unraveling in Social Networks: The Anchored k-Core Problem. SIAM J. Discret. Math., Vol. 29, 3 (2015), 1452--1475.
[6]
É douard Bonnet, Vangelis Th. Paschos, and Florian Sikora. 2016. Parameterized exact and approximation algorithms for maximum k-set cover and related satisfiability problems. RAIRO Theor. Informatics Appl., Vol. 50, 3 (2016), 227--240.
[7]
Chen Chen, Ruiyue Peng, Lei Ying, and Hanghang Tong. 2018. Network Connectivity Optimization: Fundamental Limits and Effective Algorithms. In KDD. ACM, 1167--1176.
[8]
Rajesh Hemant Chitnis, Fedor V. Fomin, and Petr A. Golovach. 2013. Preventing Unraveling in Social Networks Gets Harder. In AAAI. AAAI Press.
[9]
Edith Cohen. 1997. Size-Estimation Framework with Applications to Transitive Closure and Reachability. J. Comput. Syst. Sci., Vol. 55, 3 (1997), 441--453.
[10]
Pierluigi Crescenzi, Gianlorenzo D'Angelo, Lorenzo Severini, and Yllka Velaj. 2016. Greedily Improving Our Own Closeness Centrality in a Network. ACM Trans. Knowl. Discov. Data, Vol. 11, 1 (2016), 9:1--9:32.
[11]
Yizhou Dai, Miao Qiao, and Lijun Chang. 2022. Anchored Densest Subgraph. In SIGMOD. ACM, 1200--1213.
[12]
Palash Dey, Suman Kalyan Maity, Sourav Medya, and Arlei Silva. 2021. Network Robustness via Global k-cores. In AAMAS. 438--446.
[13]
Wendy Ellens and Robert E. Kooij. 2013. Graph measures and network robustness. CoRR, Vol. abs/1311.5064 (2013).
[14]
Scott Freitas, Diyi Yang, Srijan Kumar, Hanghang Tong, and Duen Horng Chau. 2022. Graph Vulnerability and Robustness: A Survey. IEEE TKDE (2022).
[15]
David Garc'i a, Pavlin Mavrodiev, and Frank Schweitzer. 2013. Social resilience in online communities: the autopsy of friendster. In Conference on Online Social Networks. 39--50.
[16]
Qintian Guo, Sibo Wang, Zhewei Wei, and Ming Chen. 2020. Influence Maximization Revisited: Efficient Reverse Reachable Set Generation with Bound Tightened. In SIGMOD. 2167--2181.
[17]
S Louis Hakimi. 1962. On realizability of a set of integers as degrees of the vertices of a linear graph. I. J. Soc. Indust. Appl. Math., Vol. 10, 3 (1962), 496--506.
[18]
Takanori Hayashi, Takuya Akiba, and Yuichi Yoshida. 2015. Fully Dynamic Betweenness Centrality Maintenance on Massive Networks. Proc. VLDB Endow., Vol. 9, 2 (2015), 48--59.
[19]
David Kempe, Jon M. Kleinberg, and É va Tardos. 2003. Maximizing the spread of influence through a social network. In KDD. ACM, 137--146.
[20]
Gunnar W Klau and René Weiskircher. 2005. Robustness and resilience. Network Analysis: Methodological Foundations (2005), 417--437.
[21]
Bernhard Korte and Jens Vygen. 2002. Combinatorial Optimization. Algorithms and Combinatorics (2002).
[22]
Jérôme Kunegis. [n.,d.]. The KONECT Project. http://konect.cc/.
[23]
Jure Leskovec and Andrej Krevl. 2014. SNAP Datasets: Stanford Large Network Dataset Collection. http://snap.stanford.edu/data.
[24]
Qingyuan Linghu, Fan Zhang, Xuemin Lin, Wenjie Zhang, and Ying Zhang. 2020. Global Reinforcement of Social Networks: The Anchored Coreness Problem. In SIGMOD. 2211--2226.
[25]
Kaixin Liu, Sibo Wang, Yong Zhang, and Chunxiao Xing. 2021. An Efficient Algorithm for the Anchored k-Core Budget Minimization Problem. In ICDE. IEEE, 1356--1367.
[26]
Fragkiskos D Malliaros and Michalis Vazirgiannis. 2013. To stay or not to stay: modeling engagement dynamics in social graphs. In CIKM. 469--478.
[27]
David W Matula and Leland L Beck. 1983. Smallest-last ordering and clustering and graph coloring algorithms. Journal of the ACM (JACM), Vol. 30, 3 (1983), 417--427.
[28]
Sourav Medya, Tiyani Ma, Arlei Silva, and Ambuj K. Singh. 2020. A Game Theoretic Approach For Core Resilience. In IJCAI. ijcai.org, 3473--3479.
[29]
Milena Oehlers and Benjamin Fabian. 2021. Graph Metrics for Network Robustness-A Survey. Mathematics, Vol. 9, 8 (2021), 895.
[30]
Ahmet Erdem Sariyü ce and Ali Pinar. 2016. Fast Hierarchy Construction for Dense Subgraphs. Proc. VLDB Endow., Vol. 10, 3 (2016), 97--108.
[31]
Ahmet Erdem Sariyü ce, C. Seshadhri, and Ali Pinar. 2018. Local Algorithms for Hierarchical Dense Subgraph Discovery. Proc. VLDB Endow., Vol. 12, 1 (2018), 43--56.
[32]
Stephen B Seidman. 1983. Network structure and minimum degree. Social networks, Vol. 5, 3 (1983), 269--287.
[33]
Kazunori Seki and Masataka Nakamura. 2016. The collapse of the Friendster network started from the center of the core. In ASONAM. IEEE Computer Society, 477--484.
[34]
Kazunori Seki and Masataka Nakamura. 2017. The mechanism of collapse of the Friendster network: What can we learn from the core structure of Friendster? Social Network Analysis and Mining, Vol. 7, 1 (2017), 10.
[35]
Youze Tang, Yanchen Shi, and Xiaokui Xiao. 2015. Influence Maximization in Near-Linear Time: A Martingale Approach. In SIGMOD. 1539--1554.
[36]
Casper van Elteren, Rick Quax, and Peter Sloot. 2022. Dynamic importance of network nodes is poorly predicted by static structural features. Physica A: Statistical Mechanics and its Applications (2022), 126889.
[37]
Jiadong Xie, Zehua Chen, Deming Chu, Fan Zhang, Xuemin Lin, and Zhihong Tian. 2024. Influence Maximization via Vertex Countering. Proc. VLDB Endow., Vol. 17, 6 (2024), 1297--1309.
[38]
Jiadong Xie, Fan Zhang, Kai Wang, Xuemin Lin, and Wenjie Zhang. 2023. Minimizing the Influence of Misinformation via Vertex Blocking. In 39th IEEE International Conference on Data Engineering, ICDE 2023. IEEE, 789--801.
[39]
Fan Zhang, Qingyuan Linghu, Jiadong Xie, Kai Wang, Xuemin Lin, and Wenjie Zhang. 2023. Quantifying Node Importance over Network Structural Stability. In KDD. ACM.
[40]
Fan Zhang, Jiadong Xie, Kai Wang, Shiyu Yang, and Yu Jiang. 2022. Discovering key users for defending network structural stability. World Wide Web, Vol. 25, 2 (2022), 679--701.
[41]
Fan Zhang, Wenjie Zhang, Ying Zhang, Lu Qin, and Xuemin Lin. 2017b. OLAK: An Efficient Algorithm to Prevent Unraveling in Social Networks. Proc. VLDB Endow., Vol. 10, 6 (2017), 649--660.
[42]
Fan Zhang, Ying Zhang, Lu Qin, Wenjie Zhang, and Xuemin Lin. 2017a. Finding critical users for social network engagement: The collapsed k-core problem. In AAAI, Vol. 31.
[43]
Jianwen Zhao and Yufei Tao. 2021. Minimum Vertex Augmentation. Proc. VLDB Endow., Vol. 14, 9 (2021), 1454--1466.
[44]
Kangfei Zhao, Zhiwei Zhang, Yu Rong, Jeffrey Xu Yu, and Junzhou Huang. 2023. Finding Critical Users in Social Communities via Graph Convolutions. IEEE Trans. Knowl. Data Eng., Vol. 35, 1 (2023), 456--468.
[45]
Weijie Zhu, Chen Chen, Xiaoyang Wang, and Xuemin Lin. 2018. K-core Minimization: An Edge Manipulation Approach. In CIKM. ACM, 1667--1670. io
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May 2024
4826 pages
ISBN:9798400701719
DOI:10.1145/3589334
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- Chong-Wah Ngo,
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