65 research outputs found
Mining Frequent Neighborhood Patterns in Large Labeled Graphs
Over the years, frequent subgraphs have been an important sort of targeted
patterns in the pattern mining literatures, where most works deal with
databases holding a number of graph transactions, e.g., chemical structures of
compounds. These methods rely heavily on the downward-closure property (DCP) of
the support measure to ensure an efficient pruning of the candidate patterns.
When switching to the emerging scenario of single-graph databases such as
Google Knowledge Graph and Facebook social graph, the traditional support
measure turns out to be trivial (either 0 or 1). However, to the best of our
knowledge, all attempts to redefine a single-graph support resulted in measures
that either lose DCP, or are no longer semantically intuitive.
This paper targets mining patterns in the single-graph setting. We resolve
the "DCP-intuitiveness" dilemma by shifting the mining target from frequent
subgraphs to frequent neighborhoods. A neighborhood is a specific topological
pattern where a vertex is embedded, and the pattern is frequent if it is shared
by a large portion (above a given threshold) of vertices. We show that the new
patterns not only maintain DCP, but also have equally significant semantics as
subgraph patterns. Experiments on real-life datasets display the feasibility of
our algorithms on relatively large graphs, as well as the capability of mining
interesting knowledge that is not discovered in prior works.Comment: 9 page
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
Pair-Linking for Collective Entity Disambiguation: Two Could Be Better Than All
Collective entity disambiguation aims to jointly resolve multiple mentions by
linking them to their associated entities in a knowledge base. Previous works
are primarily based on the underlying assumption that entities within the same
document are highly related. However, the extend to which these mentioned
entities are actually connected in reality is rarely studied and therefore
raises interesting research questions. For the first time, we show that the
semantic relationships between the mentioned entities are in fact less dense
than expected. This could be attributed to several reasons such as noise, data
sparsity and knowledge base incompleteness. As a remedy, we introduce MINTREE,
a new tree-based objective for the entity disambiguation problem. The key
intuition behind MINTREE is the concept of coherence relaxation which utilizes
the weight of a minimum spanning tree to measure the coherence between
entities. Based on this new objective, we design a novel entity disambiguation
algorithms which we call Pair-Linking. Instead of considering all the given
mentions, Pair-Linking iteratively selects a pair with the highest confidence
at each step for decision making. Via extensive experiments, we show that our
approach is not only more accurate but also surprisingly faster than many
state-of-the-art collective linking algorithms
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