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Stanislav Funiak

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3 papers

2 author rows

JMLR Journal 2017 Journal Article

Joint Label Inference in Networks

Deepayan Chakrabarti
Stanislav Funiak
Jonathan Chang
Sofus A. Macskassy

We consider the problem of inferring node labels in a partially labeled graph where each node in the graph has multiple label types and each label type has a large number of possible labels. Our primary example, and the focus of this paper, is the joint inference of label types such as hometown, current city, and employers for people connected by a social network; by predicting these user profile fields, the network can provide a better experience to its users. Existing approaches such as Label Propagation (Zhu et al., 2003) fail to consider interactions between the label types. Our proposed method, called EDGEEXPLAIN explicitly models these interactions, while still allowing scalable inference under a distributed message- passing architecture. On a large subset of the Facebook social network, collected in a previous study (Chakrabarti et al., 2014), EDGEEXPLAIN outperforms label propagation for several label types, with lifts of up to $120\%$ for recall@1 and $60\%$ for recall@3. [abs] [ pdf ][ bib ] &copy JMLR 2017. ( edit, beta )

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ICML Conference 2014 Conference Paper

Joint Inference of Multiple Label Types in Large Networks

Deepayan Chakrabarti
Stanislav Funiak
Jonathan Chang
Sofus A. Macskassy

We tackle the problem of inferring node labels in a partially labeled graph where each node in the graph has multiple label types and each label type has a large number of possible labels. Our primary example, and the focus of this paper, is the joint inference of label types such as hometown, current city, and employers, for users connected by a social network. Standard label propagation fails to consider the properties of the label types and the interactions between them. Our proposed method, called EdgeExplain, explicitly models these, while still enabling scalable inference under a distributed message-passing architecture. On a billion-node subset of the Facebook social network, EdgeExplain significantly outperforms label propagation for several label types, with lifts of up to 120% for recall@1 and 60% for recall@3.

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NeurIPS Conference 2006 Conference Paper

Distributed Inference in Dynamical Systems

Stanislav Funiak
Carlos Guestrin
Rahul Sukthankar
Mark Paskin

We present a robust distributed algorithm for approximate probabilistic inference in dynamical systems, such as sensor networks and teams of mobile robots. Using assumed density filtering, the network nodes maintain a tractable representation of the belief state in a distributed fashion. At each time step, the nodes coordinate to condition this distribution on the observations made throughout the network, and to advance this estimate to the next time step. In addition, we identify a significant challenge for probabilistic inference in dynamical systems: message losses or network partitions can cause nodes to have inconsistent beliefs about the current state of the system. We address this problem by developing distributed algorithms that guarantee that nodes will reach an informative consistent distribution when communication is re-established. We present a suite of experimental results on real-world sensor data for two real sensor network deployments: one with 25 cameras and another with 54 temperature sensors.

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