, 75 6.2 A Unified Framework For Data-Driven And User-Driven Geolocated Event Discovery, Taking Into Account User Feedback Into Biased Quality Measures: The Case of Geolocated Event Detection In Social Media Contents 6.1 Introduction
, Integration Of User Feedback Into Quality Measure
, 81 6.4.1 Event Detection With Coverage Guarantee, Algorithms For Computing
, 87 6.5.4 User-driven discovery of geo-located events
,
, As such, they are an incredibly rich mean to know the pulse of the world, or of a specific neighborhood, in real time. Analyzing the abundant user-generated content can provide high valued information. Social media data have been analysed for several purposes, e.g. to understand the concerns of a population, Introduction Social microblogging, 2010.
, Cohesive Subgraphs with Exceptional Attributes (CSEA), Mining Subjectively Interesting Attributed Subgraphs Contents 7.1 Introduction
,
,
, 109 graph embeddings [Cai et al., 2017]-which map the nodes of a graph into a low dimensional space while preserving the local and global graph structure as well as possible-, community detection [Fortunato, 2010]-the discovery of groups of vertices that somehow 'belong together'-, or subgraph mining-the identification of informative subgraphs. Besides the relational structure, the so-called attributed graphs may carry information in the form of attribute-value pairs on vertices and/or edges
, U)) is the description length of S (resp. U
, Encoding an attribute over |A| possibilities costs log(|A|) bits. We do this encoding (|S| + 1) times, one for each attribute in S plus one for the length of S. The second term is the length of the encoding of restriction (a, with M a = |{â|{?|{â(v) | v ? V }|, the number of distinct values of a on the graph
, As mentioned above, we describe the vertex set U in the pattern as (the intersection of) a set of neighborhoods N d (v), v ? V , with a set of exceptions: vertices are in the intersection but not part of U. The length of such a description is the sum of the description lengths of the neighborhoods and the exceptions. More formally, let us define the set of all neighborhoods N = {N d (v) | v ? V ? d ? 0, D} (with D the maximum range d considered), and let N (U) = {N d (v) subset X ? N (U), along with the set of exceptions exc(X,U) ? N d (v)?X N d (v) \U, ]) and the encoding of the other bound of the interval is in logarithm of the number of distinct values of a on the graph
, |N |)). The second term accounts for the description of the number of exceptions (log(| ? x?X x|)), and for describing the exceptions themselves, first term accounts for the description of the number of neighborhoods (log(|N |), and for describing which neighborhoods are involved (|X| log
, there is generally no unique way to describe the set U. The best one is thus the one that minimizes f
,
, ) that are closed simultaneously with respect to U, S, and the neighborhood description. Second, it ranks patterns according to their SI values. The calculation of IC(U, S) and DL A (S) is simple and direct. However, computing DL V (U) is not trivial, since there are several ways to describe U and we are looking for the one minimizing f (X,U), SIAS-Miner mines interesting patterns using an enumerate-and-rank approach. First, it enumerates all CSEA patterns
, The set of exceptions in X ?Y ? {e} is equal to exc(X ?Y ? {e},U) = exc(X ? {e},U) ? exc
, then exc(X ?Y ? {e},U) ? exc(X ?Y ? {e },U)
, Notice that even if an element e has been removed due to the lower bound of e , the procedure is still correct since e is lower bound by e by the transitivity of inclusion, Algorithm, vol.7
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