OpenRaiser/Intern-Atlas huggingface dataset:
Explainable classification of brain networks via contrast subgraphs
Authors: Tommaso Lanciano, Francesco Bonchi, Aristides Gionis
Venue: Conference KDD 2020
Year: 2020

Mining human-brain networks to discover patterns that can be used to discriminate between healthy individuals and patients affected by some neurological disorder, is a fundamental task in neuroscience. Learning simple and interpretable models is as important as mere classification accuracy. In this paper we introduce a novel approach for classifying brain networks based on extracting contrast subgraphs, i.e., a set of vertices whose induced subgraphs are dense in one class of graphs and sparse in the other. We formally define the problem and present an algorithmic solution for extracting contrast subgraphs. We then apply our method to a brain-network dataset consisting of children affected by Autism Spectrum Disorder and children Typically Developed. Our analysis confirms the interestingness of the discovered patterns, which match background knowledge in the neuroscience literature. Further analysis on other classification tasks confirm the simplicity, soundness, and high explainability of our proposal, which also exhibits superior classification accuracy, to more complex state-of-the-art methods.
Locally densest subgraph discovery
Authors: Lu Qin, Rong-Hua Li, Lijun Chang, Chengqi Zhang
Venue: Conference KDD 2015
Year: 2015

Mining dense subgraphs from a large graph is a fundamental graph mining task and can be widely applied in a variety of application domains such as network science, biology, graph database, web mining, graph compression, and micro-blogging systems. Here a dense subgraph is defined as a subgraph with high density (#.edge / #.node). Existing studies of this problem either focus on finding the densest subgraph or identifying an optimal clique-like dense subgraph, and they adopt a simple greedy approach to find the top-k dense subgraphs. However, their identified subgraphs cannot be used to represent the dense regions of the graph. Intuitively, to represent a dense region, the subgraph identified should be the subgraph with highest density in its local region in the graph. However, it is non-trivial to formally model a locally densest subgraph. In this paper, we aim to discover top-k such representative locally densest subgraphs of a graph. We provide an elegant parameter-free definition of a locally densest subgraph. The definition not only fits well with the intuition, but is also associated with several nice structural properties. We show that the set of locally densest subgraphs in a graph can be computed in polynomial time. We further propose three novel pruning strategies to largely reduce the search space of the algorithm. In our experiments, we use several real datasets with various graph properties to evaluate the effectiveness of our model using four quality measures and a case study. We also test our algorithms on several real web-scale graphs, one of which contains 118.14 million nodes and 1.02 billion edges, to demonstrate the high efficiency of the proposed algorithms.
Peeling Bipartite Networks for Dense Subgraph Discovery
Authors: Ahmet Erdem Sariyüce, Ali Pinar
Venue: Conference WSDM 2018
Year: 2018

Finding dense bipartite subgraphs and detecting the relations among them is an important problem for affiliation networks that arise in a range of domains, such as social network analysis, word-document clustering, the science of science, internet advertising, and bioinformatics. However, most dense subgraph discovery algorithms are designed for classic, unipartite graphs. Subsequently, studies on affiliation networks are conducted on the co-occurrence graphs (e.g., co-author and co-purchase) that project the bipartite structure to a unipartite structure by connecting two entities if they share an affiliation. Despite their convenience, co-occurrence networks come at a cost of loss of information and an explosion in graph sizes, which limit the quality and the efficiency of solutions. We study the dense subgraph discovery problem on bipartite graphs. We define a framework of bipartite subgraphs based on the butterfly motif (2,2-biclique) to model the dense regions in a hierarchical structure. We introduce efficient peeling algorithms to find the dense subgraphs and build relations among them. We can identify denser structures compared to the state-of-the-art algorithms on co-occurrence graphs in real-world data. Our analyses on an author-paper network and a user-product network yield interesting subgraphs and hierarchical relations such as the groups of collaborators in the same institution and spammers that give fake ratings.
The community-search problem and how to plan a successful cocktail party
Authors: Mauro Sozio, Aristides Gionis
Venue: Conference KDD 2010
Year: 2010

A lot of research in graph mining has been devoted in the discovery of communities. Most of the work has focused in the scenario where communities need to be discovered with only reference to the input graph. However, for many interesting applications one is interested in finding the community formed by a given set of nodes. In this paper we study a query-dependent variant of the community-detection problem, which we call the community-search problem: given a graph G, and a set of query nodes in the graph, we seek to find a subgraph of G that contains the query nodes and it is densely connected.
A correlation clustering framework for community detection
Authors: Nate Veldt, David F. Gleich, Anthony Wirth
Venue: Conference WWW 2018
Year: 2018

Graph clustering, or community detection, is the task of identifying groups of closely related objects in a large network. In this paper we introduce a new community detection framework called LambdaCC that is based on a specially weighted version of correlation clustering. A key component in our methodology is a clustering resolution parameter, lambda, which implicitly controls the size and structure of clusters formed by our framework. We show that, by increasing this parameter, our objective effectively interpolates between two different strategies in graph clustering: finding a sparse cut and forming dense subgraphs. Our methodology unifies and generalizes a number of other important clustering quality functions including modularity, sparsest cut, and cluster deletion, and places them all within the context of an optimization problem that has been well studied from the perspective of approximation algorithms. Our approach to clustering is particularly relevant in the regime of finding dense clusters, as it leads to a 2-approximation for the cluster deletion problem. We use our approach to cluster several graphs, including large collaboration networks and social networks.
Community interaction and conflict on the web
Authors: Srijan Kumar, William L. Hamilton, Jure Leskovec, Dan Jurafsky
Venue: Conference WWW 2018
Year: 2018

Users organize themselves into communities on web platforms. These communities can interact with one another, often leading to conflicts and toxic interactions. However, little is known about the mechanisms of interactions between communities and how they impact users. Here we study intercommunity interactions across 36,000 communities on Reddit, examining cases where users of one community are mobilized by negative sentiment to comment in another community. We show that such conflicts tend to be initiated by a handful of communities---less than 1% of communities start 74% of conflicts. While conflicts tend to be initiated by highly active community members, they are carried out by significantly less active members. We find that conflicts are marked by formation of echo chambers, where users primarily talk to other users from their own community. In the long-term, conflicts have adverse effects and reduce the overall activity of users in the targeted communities. Our analysis of user interactions also suggests strategies for mitigating the negative impact of conflicts---such as increasing direct engagement between attackers and defenders. Further, we accurately predict whether a conflict will occur by creating a novel LSTM model that combines graph embeddings, user, community, and text features. This model can be used to create an early-warning system for community moderators to prevent conflicts. Altogether, this work presents a data-driven view of community interactions and conflict, and paves the way towards healthier online communities.
SIR-Hawkes: Linking Epidemic Models and Hawkes Processes to Model Diffusions in Finite Populations
Authors: Marian-Andrei Rizoiu, Swapnil Mishra, Quyu Kong, Mark James Carman, Lexing Xie
Venue: Conference WWW 2018
Year: 2018

Among the statistical tools for online information diffusion modeling, both epidemic models and Hawkes point processes are popular choices. The former originate from epidemiology, and consider information as a viral contagion which spreads into a population of online users. The latter have roots in geophysics and finance, view individual actions as discrete events in continuous time, and modulate the rate of events according to the self-exciting nature of event sequences. Here, we establish a novel connection between these two frameworks. Namely, the rate of events in an extended Hawkes model is identical to the rate of new infections in the Susceptible-Infected-Recovered (SIR) model after marginalizing out recovery events -- which are unobserved in a Hawkes process. This result paves the way to apply tools developed for SIR to Hawkes, and vice versa. It also leads to HawkesN, a generalization of the Hawkes model which accounts for a finite population size. Finally, we derive the distribution of cascade sizes for HawkesN, inspired by methods in stochastic SIR. Such distributions provide nuanced explanations to the general unpredictability of popularity: the distribution for diffusion cascade sizes tends to have two modes, one corresponding to large cascade sizes and another one around zero.
Modeling dynamic behavior in large evolving graphs
Authors: Ryan A. Rossi, Brian Gallagher, Jennifer Neville, Keith Henderson
Venue: Conference WSDM 2013
Year: 2013

Given a large time-evolving graph, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model the behavioral transition patterns of nodes? We propose a temporal behavior model that captures the "roles" of nodes in the graph and how they evolve over time. The proposed dynamic behavioral mixed-membership model (DBMM) is scalable, fully automatic (no user-defined parameters), non-parametric/data-driven (no specific functional form or parameterization), interpretable (identifies explainable patterns), and flexible (applicable to dynamic and streaming networks). Moreover, the interpretable behavioral roles are generalizable and computationally efficient. We applied our model for (a) identifying patterns and trends of nodes and network states based on the temporal behavior, (b) predicting future structural changes, and (c) detecting unusual temporal behavior transitions. The experiments demonstrate the scalability, flexibility, and effectiveness of our model for identifying interesting patterns, detecting unusual structural transitions, and predicting the future structural changes of the network and individual nodes.
Ads Keyword Rewriting Using Search Engine Results
Authors: Javad Azimi, Adnan Alam, Ruofei Zhang
Venue: Conference WWW 2015
Year: 2015

Paid Search (PS) ads are one of the main revenue sources of online advertising companies where the goal is returning a set of relevant ads for a searched query in search engine websites such as Bing. Typical PS algorithms, return the ads which their Bided Keywords (BKs) are a subset of searched queries or relevant to them. However, there is a huge gap between BKs and searched queries as a considerable amount of BKs are rarely searched by the users. This is mostly due to the rare BKs provided by advertisers. In this paper, we propose an approach to rewrite the rare BKs to more commonly searched keywords, without compromising the original BKs intent, which increases the coverage and depth of PS ads and thus it delivers higher monetization power. In general, we first find the relevant web documents pertaining to the BKs and then extract common keywords using the web doc title and its summary snippets. Experimental results show the effectiveness of proposed algorithm in rewriting rare BKs and consequently providing us with a significant improvement in recall and thereby revenue.
Scalable query n-gram embedding for improving matching and relevance in sponsored search
Authors: Xiao Bai, Erik Ordentlich, Yuanyuan Zhang, Andy Feng, Adwait Ratnaparkhi, Reena Somvanshi, Aldi Tjahjadi
Venue: Conference KDD 2018
Year: 2018

Sponsored search has been the major source of revenue for commercial web search engines. It is crucial for a sponsored search engine to retrieve ads that are relevant to user queries to attract clicks as advertisers only pay when their ads get clicked. Retrieving relevant ads for a query typically involves in first matching related ads to the query and then filtering out irrelevant ones. Both require understanding the semantic relationship between a query and an ad. In this work, we propose a novel embedding of queries and ads in sponsored search. The query embeddings are generated from constituent word n-gram embeddings that are trained to optimize an event level word2vec objective over a large volume of search data. We show through a query rewriting task that the proposed query n-gram embedding model outperforms the state-of-the-art word embedding models for capturing query semantics. This allows us to apply the proposed query n-gram embedding model to improve query-ad matching and relevance in sponsored search. First, we use the similarity between a query and an ad derived from the query n-gram embeddings as an additional feature in the query-ad relevance model used in Yahoo Search. We show through online A/B test that using the new relevance model to filter irrelevant ads offline leads to 0.47% CTR and 0.32% revenue increase. Second, we propose a novel online query to ads matching system, built on an open-source big-data serving engine [30], using the learned query n-gram embeddings. Online A/B test shows that the new matching technique increases the search revenue by 2.32% as it significantly increases the ad coverage for tail queries.
Search advertising using web relevance feedback
Authors: Andrei Z. Broder, Peter Ciccolo, Marcus Fontoura, Evgeniy Gabrilovich, Vanja Josifovski, Lance Riedel
Venue: Conference CIKM 2008
Year: 2008

The business of Web search, a $10 billion industry, relies heavily on sponsored search, whereas a few carefully-selected paid advertisements are displayed alongside algorithmic search results. A key technical challenge in sponsored search is to select ads that are relevant for the user's query. Identifying relevant ads is challenging because queries are usually very short, and because users, consciously or not, choose terms intended to lead to optimal Web search results and not to optimal ads. Furthermore, the ads themselves are short and usually formulated to capture the reader's attention rather than to facilitate query matching.
A semantic approach to contextual advertising
Authors: Andrei Z. Broder, Marcus Fontoura, Vanja Josifovski, Lance Riedel
Venue: Conference SIGIR 2007
Year: 2007

Contextual advertising or Context Match (CM) refers to the placement of commercial textual advertisements within the content of a generic web page, while Sponsored Search (SS) advertising consists in placing ads on result pages from a web search engine, with ads driven by the originating query. In CM there is usually an intermediary commercial ad-network entity in charge of optimizing the ad selection with the twin goal of increasing revenue (shared between the publisher and the ad-network) and improving the user experience. With these goals in mind it is preferable to have ads relevant to the page content, rather than generic ads. The SS market developed quicker than the CM market, and most textual ads are still characterized by "bid phrases" representing those queries where the advertisers would like to have their ad displayed. Hence, the first technologies for CM have relied on previous solutions for SS, by simply extracting one or more phrases from the given page content, and displaying ads corresponding to searches on these phrases, in a purely syntactic approach. However, due to the vagaries of phrase extraction, and the lack of context, this approach leads to many irrelevant ads. To overcome this problem, we propose a system for contextual ad matching based on a combination of semantic and syntactic features.
Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising
Authors: Mihajlo Grbovic, Nemanja Djuric, Vladan Radosavljevic, Fabrizio Silvestri, Ricardo Baeza-Yates, Andrew Feng, Erik Ordentlich, Lee Yang, Gavin Owens
Venue: Conference SIGIR 2016
Year: 2016

Sponsored search represents a major source of revenue for web search engines. This popular advertising model brings a unique possibility for advertisers to target users' immediate intent communicated through a search query, usually by displaying their ads alongside organic search results for queries deemed relevant to their products or services. However, due to a large number of unique queries it is challenging for advertisers to identify all such relevant queries. For this reason search engines often provide a service of advanced matching, which automatically finds additional relevant queries for advertisers to bid on. We present a novel advanced matching approach based on the idea of semantic embeddings of queries and ads. The embeddings were learned using a large data set of user search sessions, consisting of search queries, clicked ads and search links, while utilizing contextual information such as dwell time and skipped ads. To address the large-scale nature of our problem, both in terms of data and vocabulary size, we propose a novel distributed algorithm for training of the embeddings. Finally, we present an approach for overcoming a cold-start problem associated with new ads and queries. We report results of editorial evaluation and online tests on actual search traffic. The results show that our approach significantly outperforms baselines in terms of relevance, coverage, and incremental revenue. Lastly, we open-source learned query embeddings to be used by researchers in computational advertising and related fields.
Context-and content-aware embeddings for query rewriting in sponsored search
Authors: Mihajlo Grbovic, Nemanja Djuric, Vladan Radosavljevic, Fabrizio Silvestri, Narayan Bhamidipati
Venue: Conference SIGIR 2015
Year: 2015

Search engines represent one of the most popular web services, visited by more than 85% of internet users on a daily basis. Advertisers are interested in making use of this vast business potential, as very clear intent signal communicated through the issued query allows effective targeting of users. This idea is embodied in a sponsored search model, where each advertiser maintains a list of keywords they deem indicative of increased user response rate with regards to their business. According to this targeting model, when a query is issued all advertisers with a matching keyword are entered into an auction according to the amount they bid for the query, and the winner gets to show their ad. One of the main challenges is the fact that a query may not match many keywords, resulting in lower auction value, lower ad quality, and lost revenue for advertisers and publishers. Possible solution is to expand a query into a set of related queries and use them to increase the number of matched ads, called query rewriting. To this end, we propose rewriting method based on a novel query embedding algorithm, which jointly models query content as well as its context within a search session. As a result, queries with similar content and context are mapped into vectors close in the embedding space, which allows expansion of a query via simple K-nearest neighbor search in the projected space. The method was trained on more than 12 billion sessions, one of the largest corpuses reported thus far, and evaluated on both public TREC data set and in-house sponsored search data set. The results show the proposed approach significantly outperformed existing state-of-the-art, strongly indicating its benefits and the monetization potential.
Improving ad relevance in sponsored search
Authors: Dustin Hillard, Stefan Schroedl, Eren Manavoglu, Hema Raghavan, Chris Leggetter
Venue: Conference WSDM 2010
Year: 2010

We describe a machine learning approach for predicting sponsored search ad relevance. Our baseline model incorporates basic features of text overlap and we then extend the model to learn from past user clicks on advertisements. We present a novel approach using translation models to learn user click propensity from sparse click logs.
Rare query expansion through generative adversarial networks in search advertising
Authors: Mu-Chu Lee, Bin Gao, Ruofei Zhang
Venue: Conference KDD 2018
Year: 2018

Generative Adversarial Networks (GAN) have achieved great success in generating realistic synthetic data like images, tags, and sentences. We explore using GAN to generate bid keywords directly from query in sponsored search ads selection, especially for rare queries. Specifically, in the query expansion (query-keyword matching) scenario in search advertising, we train a sequence to sequence model as the generator to generate keywords, conditioned on the user query, and use a recurrent neural network model as the discriminator to play an adversarial game with the generator. By applying the trained generator, we can generate keywords directly from a given query, so that we can highly improve the effectiveness and efficiency of query-keyword matching based ads selection in search advertising. We trained the proposed model in the clicked query-keyword pair dataset from a commercial search advertising system. Evaluation results show that the generated keywords are more relevant to the given query compared with the baseline model and they have big potential to bring extra revenue improvement.
Impedance coupling in content-targeted advertising
Authors: Berthier A. Ribeiro-Neto, Marco Cristo, Paulo Braz Golgher, Edleno Silva de Moura
Venue: Conference SIGIR 2005
Year: 2005

The current boom of the Web is associated with the revenues originated from on-line advertising. While search-based advertising is dominant, the association of ads with a Web page (during user navigation) is becoming increasingly important. In this work, we study the problem of associating ads with a Web page, referred to as content-targeted advertising, from a computer science perspective. We assume that we have access to the text of the Web page, the keywords declared by an advertiser, and a text associated with the advertiser's business. Using no other information and operating in fully automatic fashion, we propose ten strategies for solving the problem and evaluate their effectiveness. Our methods indicate that a matching strategy that takes into account the semantics of the problem (referred to as AAK for "ads and keywords") can yield gains in average precision figures of 60% compared to a trivial vector-based strategy. Further, a more sophisticated impedance coupling strategy, which expands the text of the Web page to reduce vocabulary impedance with regard to an advertisement, can yield extra gains in average precision of 50%. These are first results. They suggest that great accuracy in content-targeted advertising can be attained with appropriate algorithms.