The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Data Mining for Co-Evolving Time Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
The dynamics of viral marketing
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Study on Representation of Time Series Based on Subsection Polynomial Fitting
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Identifying the influential bloggers in a community
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Extracting influential nodes for information diffusion on a social network
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
The web as a graph: measurements, models, and methods
COCOON'99 Proceedings of the 5th annual international conference on Computing and combinatorics
Inferring networks of diffusion and influence
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Community-based greedy algorithm for mining top-K influential nodes in mobile social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding effectors in social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Online discovery and maintenance of time series motifs
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
Detecting leaders from correlated time series
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
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In an interconnected and dynamic world, the evolution of one entity may cause a series of significant value changes for some others. For example, the currency inflation of Thailand caused the currency slump of other Asian countries, which eventually led to the financial crisis of 1997. We call such high impact entities shakers. To discover shakers, we first introduce the concept of a cascading graph to capture the causality relationships among evolving entities over some period of time, and then infer shakers from the graph. In a cascading graph, nodes represent entities and weighted links represent the causality effects. In order to find hidden shakers in such a graph, two scoring functions are proposed, each of which estimates how much the target entity can affect the values of some others. The idea is to artificially inject a significant change on the target entity, and estimate its direct and indirect influence on the others, by following an inference rule under the Markovian assumption. Both scoring functions are proven to be only dependent on the structure of a cascading graph and can be calculated in polynomial time. Experiments included three datasets in social sciences. Without directly applicable previous methods, we modified three graphical models as baselines. The two proposed scoring functions can effectively capture those high impact entities. For example, in the experiment to discover stock market shakers, the proposed models outperform the three baselines by as much as 50% in accuracy with the ground truth obtained from Yahoo!~Finance.