Enhancing Deep Consistent Graph Metric with Affinity and Alignment for Incremental Social Event Detection using Cross-Layer Attention
Authors: Shraban Kumar Chatterjee
Shubham Gupta
Suman Kundu
Venue: Accepted by TMLR ()

Abstract: Existing methods of event detection from social media (i.e., X), for instance, KPGNN, FinEvent, and CLKD, use triplet loss for feature separation. Triplet loss suffers from two notable discrepancies in the latent space: (i) inconsistency in intra-event and inter-event distances, and (ii) an inability to ensure the closeness of messages from the same event across different mini-batches. The present paper proposes two novel loss functions to improve consistency in the latent space. The first loss function guarantees consistent intra-event and inter-event distances by increasing the affinity between intra-event points. On the other hand, the alignment loss enhances the cosine similarity between the feature space and label space, thereby aligning features of the same event class across diverse mini-batches. We provide theoretical justification that the proposed loss ensures discriminative features in the latent space, like CGML, without its costly pairwise or specialised batching. Adding to our loss function, we introduce a new attention module designed to effectively address heterogeneous relations without necessitating a separate optimisation objective. Through comprehensive experimentation on two publicly available datasets, we have shown an average improvement of $24.05\%$, $27.23\%$ and $123.69\%$ in NMI, AMI and ARI, respectively, over supervised SOTA event detection methods. Our method also shows improvements over SOTA unsupervised event detection methods across both datasets. These are supported by statistical significance tests. Generalizability of the proposed loss in general clustering problem in graph domain is shown through experiments.

Published in Transactions on Machine Learning Research (02/2026)
Enhancing Deep Consistent Graph Metric with Affinity and Alignment for Incremental Social Event Detection using Cross-Layer Attention
Shraban Kumar Chatterjee
Chatterjee.2@iie 20iitj.a...... ac ac.in
Department of Computer Science & Engineering Indian Institute of Technology Jodkpur
Shubham Gupta
gupta.870iity 370sttj. oc in
Department of Computer Science & Engineering Indian Institute of Technology Jodhpur
Suman Kundu
soman@ddai.iitmm.ommm
Wadhwoni School of Data Science & AI Indian Institute of Technology Modras
Reviewed on OpenReview: https: // openreview. net/
net/forum? forum? id= 7
Abstract
Existing methods of event detection from social media (i.e., X), for instance, KPGNN, FinEvent, and CLKD, triplct loss for feature separation. Triplet loss suffers from two notable discrepancies in the latent space: (i) inconsistency in intra-event and inter-event distances, and (ii) an inability to ensure the closeness of messages from the same event across different mini-batches. The present paper proposes two novel loss functions to improve consistency the latent space. The first loss function guarantees consistent intra-event and inter-event distances by increasing the affinity between intra-event points. On the other hand, the alignment loss enhances the cosine similarity between the feature space and label space, thereby aligning features of the same event class across diverse mini-batches. We provide theoretical justification that the proposed loss ensures discriminative features in the latent space, like CGML, without its costly pairwisc or specialised batching. Adding to our loss function, we introduce a new attention module designed to effectively address heterogeneous relations without necessitating a separate optimisation objective. Through comprehensive experimentation on two publicly available datasets, we have shown an average improvement of 24.00 24.05%, 27.23% and 123.69% in NMI, AMI and ARI, respectively, over supervised SOTA event. detection methods. Our method also shows improvements SOTA unsupervised event detection methods across both datasets. These are supported by statistical significance tests. Generalizability of the proposed loss in general clustering problem in graph domain is shown through experiments.
Introduction
Events in the real world often manifest in the virtual world, in the form of discussions spanning from gov- ernment policies to socio-economic status, from summer schools to the European League. These discussions open the avenue for grouping messages on social media into meaningful events. The problem of finding events from social media known as the event detection problem. Event detection can help in public opinion-basod policy making (Mao al.] 2024), crisis management (Saini al., 2024 Zhang et al.) (022b)) sentiment analysis (PETRESCU et. al. 2024 and risk management, Zhang et. al. (022a)), among others. Detecting

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G2
Target Embedding
(a) Triplet Loss
(b) Proposed Loss
Figure Comparison between triplet loss and proposed loss. Here, the circles represent the feature space and the rectangles represent the label space.
events from social messages challenging, specifically due to the large volume of incoming messages that social event can generate. Another challenge to determine the relevance of events based on the span of time. As time passes, new events emerge, while older ones fade from discussion. Furthermore, unrelated events often share similar text features, utilising the same sets of words or common hashtags. Finally, social messages contain heterogeneous altributes, including timestamps and user mentions.
In order to address the challenges of scale and event relevance in large message graphs, KPGNN (Cao et al, 2021) partitions the graph into smaller blocks. They employ triplet loss function to achieve feature separa- tion. However, this approach does not account for heterogeneons attributes within the text. Building upon KPGNN, FinEvent (Peng et al.) 2023) incorporates the heterogeneous characteristies by using agentic framework to select the most salient edges. This method, however, introduces considerable computational overhead. It requires an agent for each heterogeneons relation, and each agent must calculate a reward function based on neighbourhood sorted by Euclidean distance from the relation graph. Furthermore, the reward function requires a separate optimisation objective, necessitating the generation of ground truth via K-Means for each relation lype. For feature generation, uses the same triplet loss as KPGNN and attempts mitigate class imbalance by increasing the ratio of negative to positive samples. CLKD (Ren also extends KPGNN, adapting it for pross-lingus event detection, but it, too, depends the triplet loss for feature separation. A common characteristic of KPGNN, FinEvent, and CLKD is their fundamental reliance a triplet loss framework for creating discriminative feature repnesentations. The authors of CGMT, (Chen) et al. 2021) have addressed some limitations of triplet loss by introducing the concept of a discriminative graph. However, to arrive at a discriminative graph, they need a comparison between many graph minibatch
2024)
Table 1: Related Work and Baselines
BERT
BILTMM
EventX
KPGNN
Fin Event
CLKD
HISEvent
HyperSED
Ours
Minibatch Training
X
Relevance
X
x
Incremental online learning
X
X
X
X
X
Heterogenenus relations
X
X
X
X
xxx
X
X
Training nn partial Blocks
X
X
xx
X
edge augmentation
/
/
X
Decoupled relation objective
Supervised/ Semi-supervised
X
V
/
X
X
2

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Published in Transactions on Machine Learning Research (02/2026)
pairs. This pairwise sampling is costly, especially at high volumes. Also, CGML requires batches with the same number of events in each pair.
We have identified two limitations of the triplet loss as highlighted in Figure la The first is the incon- sistency between the intra-event and inter-event distances. Depending on the triplet selection mechanism, in many cases, intra-event feature distance can be comparable to the smallest inter-event feature distance. The same is illustrated in the minibatch G2, where the highlighted yellow and red nodes show inconsistent distances. The second issue is due to the fact that the triplet loss cannot ensure the closeness between nodes of the same event across minibatches. The green line shows the disparity for the green nodes. Some green nodes are closer to yellow (or red) nodes as compared to other green nodes across minibatches.
The present paper proposes two losses, namely, affinity loss and alignment loss, to solve the aforementionedd issues with triplet loss by acting as a regularizer. Our losses ensure consistent intra-event and inter-event distances. We achieve this by increasing the affinity between the intra-cvent points and incrcasing the cosine similarity between the feature and the label mapped inlo the same space. We show this by the arrows towards the label space in Figure (Ib). This approach aligns the features of the same event within and across different minibatches. Unlike CGML, where pairwise batching and comparison are required, our method works by iterating over the minibatches of graph, which is linear in the number of minibatches. We theoretically justify that the joint optimisation of the proposed affinity and alignment loss is aligned with the definition of discriminative graphs in CGML. Besides, we introduce a new attention module to extract discriminative features from heterogeneous relations for event detection. The proposed attention module does not require a separate optimisation function like FinEvent.
Related Work
One of the pioneering works in event detection is Event (Liu et al. 2020 which uses keyword commonness. It is designed to extract events from long news articles. Its dependence on TF-IDF makes it less sensitive to low-frequency keywords, which are common in social messages. Other notable works that use word com- monness in a given time frame are Li et al. (201)); Marcus et al. (2011). Topic modelling-baat based approaches Xic et al. (2013): Zhou et al. (2015): You et al. (2013) for event detection operate by assigning cach tweet probabilistic distribution over multiple latent topics. These approaches are primarily designed to work in
offline setting.
Other than the supervised methods discussed carlier, recently proposed unsupervised event detection meth- ods like HISEvent (Cao et al. 2024) and HyperSED (Yu et al. 2025) first construct a message graph and then find message combinations that minimise the 2D entropy. HISEvent is time-consuming as it uses a greedy hierarchical optimisation objective, making it difficult to apply on a large scale. Further, HISEvvnn does not support minibatching. HyperSED introduces the concept with an anchor message graph construction and designs a hyperbolic GNN with an anchor adjacency matrix reconstruction objective. HyperSED requires training on all the blocks and does not share learning across the blocks. Furthermore, these methods still require edge supplementation. All these limit its scalability in event detection. Table 1 shows a comparison of different event detection methods.
Deep Metric Learning: Deep metric learning is currently an active area of research, Pair- based formu- lations are the most popular metric learning, with works like Triplet Loss (Sohn) 2016). Contrastive loss (Sun et al. 2014), and N-Pair loss (Sohn) 2016). Instead of pairing nodes, CGML (Chen et al., 2021 | looks at it from graph pair perspective. Its optimisation function is based on increasing the feature similarity between graph minibatches. Proxy-based methods, instead of comparing data points to each other, learn a set of proxies, which serve as representatives for each class, and optimise data-to-proxy relationships. A popular work in this line is Gu et al. (2021). Some researchers have focused on improving metric learn- ing by incorporating principles from information retrieval and ranking. A notable work in this line is Ful et al. (2021), where the authors present a self-supervised auxiliary framework for improved motric lcarning in information retrieval; similarly. Liao et al. (2023) propose contextual loss that optimizes the semantic consistency of neighbor sets to enhance retrieval ranking.
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Methodology
Tweets with common words
Tweet
Foreign policy is the presidents strength. Tonight he will need to show why he deserves to be Commander-in-Che #Debate2012 #LetsTalk"
The 2nd #PresidentialDebate between President @BarackObama &amp; Governor @MittRomney takes place tonight at 9 PM EST @HofstraU! Check out!
we had earthquake in maine tonight. crazy
2013 budget: Nigeria should expect more Boko Haram insurgency
Former Yobe governor warns FG AmehDaily
love how the moderator is putting @MittRomney in his place already! #PresidentialDebate
Tweets with common entities
Video: Expectations and opinions for the second WPresidentialDebate http://t.co/zeeRDEZ
Tweets with common mentioned users
@MittRomney &amp; @PaulRyanVP you are in our prayers for huge win on Nov #RomneyRyan2012 #GOP2012 #Debate2012
#Debate2012 coming up. @BarackObama get him this time.
#PresidentialDebate #Debates
Proud my alia mater, @Hofstrau, getting all this national press for hosting its 2nd #PresidentialDebate in #RollPride #Hofstra
Before the #debatte tonight, tell candidates enough enough! WATCH:
http://t.co/22xizaaEE and SIGN: http:///.co//NNjMkk5 #debate2012
Figure 2: Example of related tweets extracted based on common words, entities, and mentioned users from the Event2012 dataset.
We have divided the tweets into blocks based on time following Cao et al. 2002 1. From each block k. we have created a Heterogeneous Multilayer Graph (HMG) Chatterjee & Kundu (0024) Hę. In each Hy, there are 4 layers, where each layer represents a relation graph with nodes representing a tweet 8,. We take 3 types of relations between tweets, namely, common entities, common words, and common users, to constitute three layers. Two nodes in layer will contain an edge if = an edge between those two nodes in any of the other 3 layers. We assume node present in all layers. One may note that common and rare words excluded from tweets while extracting relationships.
Initial Feature Generation
We extracted text and timestamp embedding Cao al. [2021) of a tweet and concatenated them for its initial embedding. Two tweets from the event arrive at different points in time. Adding timestamp information separates past and present tweets. We will use the term Xo to represent the initial features generated in this stage.
Layer Wise Feature Generation
We extract layer-wise features for node from GAT GAT Velickové et al. 2018 encoder (G, X) - Xaxi G2 E HR, parameterised by We use GAT as it weighs each incoming connection of node in a layer that complements the cross-layer attention mechanism (explained later) we design. We define the embeddings generated from the encoder model as:
x--1
=1 R( Σ
r--1 whth xpm"xx11.
SENNs
X R
Wh 2 Tl 5is;
(1)
r P
EvNi (( (1xx!_1
Here, X!-1 and x! represents the encoding of a node s; in (1- 1)th and final GAT layer respectively. H, R, 9, (WT, Wh E f), and || denote the number of attention heads, ReLU, LeakyReLU, learnable weight matrices, and concatenation operator, respectively. T represents the attention score between nodes s; and 8j- Ns, and 0 is the neighboring nodes and initial feature vector of node 8;.

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Multi-head cross-layer attention
We extract the node features for every layer (of a block) using the encoder &. Data from each layer may
not be significant for node. We can from the example, shown in Figure 2 that connections based common words introduce noise. To address this, we designed a multi-head cross-layer attention network. Our proposed network extracts important node features from each layer. We define the cross-attention for layers Gú Gy in a block as follows:
QGi KGz (oo,Kor Vo2
A(QG, Ko,, Vo2) = So
Vd
(2)
M(Qa, Kar. = =e,
Here, Q represents the query for the layer G,, K and V are the keys and values for the layer Gj. So, M, and d are the softmax function, multi-head attention, and node feature dimension, respectively. Node features from the combined layer G, contain information from all the relations. So, we use the combined layer G. as the query G, We set G5 to the desired layer with which we need to find the attention score. Querying on Ge shows how much each layer contributes to the homogencous layer. Thus, this formulation evaluates the importance of each relation in Ge-
First, we find the contribution of common entities (Ge) in the feature vector of a node. For this, we query Ge with key and value as Ge as follows:
Xo. = So [ (W
Wv.de.
(3)
Vd
Here, [WQ.Gc, and [Wx are the transformed query and key for calculating the attention score. In our case, Wv Wx use the same graph for key and query. X is single head of the entity contextualized representation. xfinal = M(x²) is the final entity contextualized node representation in the layer Ge- Similar to this, we measure the importance of common words and common user mentions in the representation of a node as follows:
[Wodoa [WoNo. Vd Wr Wy.aa
final Gu
M S
(4)
[ [WoNea | [K, Wy.K.
X/ynal
M
S.
(5)
Vd
The final node features after the cross-layer attention phase are as follows:
Kattn = (I1(EG,,
xi v/final xefinat , xxtinaa)) Watt + battn
(6)
Proposed Loss
We first outline the theoretical foundation before defining the losses. We begin by introducing the definition of Discriminative Graph from CGML.
Definition 1. Given node features X and 1 events, represent marimum intra-cvent feature distance for the e-th event ac and the minimum inter-event feature distance from the centre of the c-th event to the centre of negative event as Se. A complete graph G with edge weight between nodes and j as Siy = exp (((,))-(e))11 is called discriminative if it has ar <3c Ve l. Here the temperature parameter.
Note that, for the following proofs, we adopted the necessary condition of Proposition in CGML using the same nolations. The details of the adoption and corresponding proof are in the supplementary Here f represents our encoder E. Furthermore, the following derivations are independent of the internal components of encoder f.
Lemma Given encoded feature of a node as f(), and events C= {ct, sampled from discriménative graph with adjacency matrices S' and S" both containing |c]] S |ci] nodes for event e, Sty S when f(xx) (xz) for all xa and xz in the same event.
subgraphs G' and G"
5

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Published in Transactions on Machine Learning Research (02/2020)
Proof. When we consider 5y from S' and Ij from S" we have El(o's - -8,) s 2(1 exp( (0ج2 (from Proposition 1). In our case Og = -=1f(aa)=((;)))) so when f(x,) + f(z)), + +ic., lim)(x)) l1)()))) f(2,)1B = O. Thus, E[(e)) - " + 0 indicating 5'y = sng..
Lemma 2. Given encoded feature of a node as I()), and events C = {er, C2, (k),..,(); subgruphs G' and G" sampled from discriménative graph G with adjacency matrices S' and S", both containing lc'l S |ei nodes for event ca, Sig = S when the same event and zi and 3 belong to different events.
- 0 and ( f(;)) - where I, and x belongs to
Proof. We take the inequality 1848 - S 2(b-a)
log($)
(from Proposition 1). Here a = exp (- (ar +2m)
40
and 6 exp (- (2m (2m-a, a.-aa-0 - where π is the maximum inter-class distance between any two events. From the Lemma we have 0e = =I((f(ze) f ((x - 0 and ag = [l(f(xx) - f(x([( ((x))11B) + 0 where (Ic, x'd) and (ax, x
40
belong to events c and & respectively making inter-event. distance π = (11)(=;;) - ((x))) + 1. Therefore,
(axtax+2x)*
lim (0.0)))))000000) exp (- [2 (-(mm-a--a))) = exp (-)). From this
(000)))))0000000 exp
10
2 + 100(2)
we can derive limo-+a 2(b a)
O. Thus we prove that 6/g Rs 8/.
Lemma (Sufficiency Condition). Given any independent subgraph G' of G with adjacency matrix S containing lcl lcal nodes V event G, C (q, C2 .... ok), G is discriminatory if [I(f(z)) f2()) 0 VJ of the same event and (11/())) f(x)) - Vei, in different events hold.
Proof. (Proof by contradiction:) Given the conditions of the Lemma let us assume that the graph G' is not discriminatory. This means, 3e; such that: 0ş 8, From [I(f(z;) - f=()1BB - O, have 0ş - 0, Ve,. From (z)f()))) f (=1))]BB) - 1 we have the squared distance between points in different event approaching 1. Since Qg + O, each class collapses to its center C,. Thus, the inter-class margin 8 for an event e, is bounded by a positive constant B, = 1 - 1 > 1, V.,,, Ot- Based on our initial assumption this implies 021 1, Vc, Thus we reach a contradiction. Thorefore, the conditions of the Lemma are sufficient to ensure a Discriminative Graph.
Remark 1 (Any graph G that satisfies Lemma 3 also satisfies the necessary condition of Proposition 1). There always exists onother subgraph G" (with adjacency matrix S") with C = {c1, C22 c,.... Ck3 events that can randomly sampled from G, where both G' and G" have (e| < |cgl points in event c, As [|(((z))
and (x)) - [(x;)]1B) 1, we have from Lemmas and S" S". Lemma 4. S', S", and L. matrices RRxx. If 1 O and 1|5" Ll 0, then - + 0.
Proof. Proof in supplementary.
Lemma 5. Given encoders Fr and Fyz we sample (iid) |4| < |cs| nodes from each event o; two times with feature matrices X', X" E R +lc))) and adjacency matrices S' and S" respectively then (S' FS" < - (S" F,y(L)) Here L denotes the label vector where 4 E L denotes that node x E C has label 1,
F,(())
Proof. For this proof, we assume that the nodes in X' and X" are arranged in the same order of events. Since both X' and X" have the same number of events with |c|| nodes in an event we have Fy(L') = FL()") = (T.).. Thus, from the inequality ||4 B|E S I|A - Cllr + B| - Cllr (Proof in supplementary) we have:
(x(x') FF((S" ~ F( FF(( (5' - (T.)
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Published in Transactions on Machine Learning Research (02/2026)
Affinity Loss:
Based on Lemmas 2 and 3 we design Affinity Loss with the aim of taking intra-class target embedding
distances close to 0 and inter-class target embedding distances towards 1. This will help us approach a discriminative graph with better event separation. We define our proposed affinity loss function as follows:
Laffinity feature Stabet ll2
||=; - zÿ 1|
(7)
Steature (1,3) = exp
if i = (diagonal excluded)
(iabat
mi
if i + j and yi = (same labels)
m2
if i and Y/ # (different labels)
2/ - xyl| Stabel(;,
2
Lasfsniry"
exp
(8)
When we set m. 1 and m2 0, finity will be closer to for pair of nodes when embeddings of the same clusters (Stabel(ij) 1) become closer (exp(- 4)). It will also approach zero when embeddings of different clusters (Stabel (ij) = 0) become farther (exp(- t)). In the case when embeddings of a node pair in different clusters come close (Stabet(ij) = O, exp(- 4)) embeddings of the nodes in the same cluster become different (Stabet(i)) = 0, exp( 4)), the loss value will tend towards one. We use f and 1 to indicate higher and lower values, respectively. The temperature parameter a allows us adjust the sensitivity, especially in cases where we may not want penalise inter-cluster distances till a threshold. In our case, we set our temperature value to
Alignment Loss:
Based on Lemmas E and 5 we define an alignment loss function to encourage alignment between the feature space (Nattn) and the label space (T.). This alignment is measured using cosine similarity between learned features and label emboddings. It defined as:
1 Σ (Zi)
Calign
The embedding functions fz and fy map input features 2, and (integer) label y into a shared latent space using two-layer neural networks:
fa(x.) = w(x). R(W((") 2, + ((n)) ) (5m) fu(v:) w(") R(W!") Vi + =b()) 1(9))
(10)
(11)
Let e, - fx())) E Rd-1 and (v) = fo (v.). The cosine similarity between the two embeddings is then given by:
(c) (x) ||e (3) 2|2 l|e!
(os()
(12)
Substituting into the loss, we obtain:
(( T2)T (())
Latign
(13)
(z) |le ! 112
This loss function encourages directional similarity between the learned feature and label embeddings for each node. When the embeddings perfectly aligned (i.e., the angle between them is 0), the cosine similarity is

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Laslinity
Lerip
Culion
Eay
C fiinal
Chastring
G
Events
Figure 3: The architecture of the proposed model.
and the loss contribution is O. When the embeddings are orthogonal, the cosine similarity becomes 0, and the loss contribution is 1. In the worst case, when the embeddings point in opposite directions, the cosine similarity becomes -1, resulting in a loss contribution 2. Note that, a new event class when appear during ineremental-training phase is assigned with new integer number and mapped to the same label space by fy keeping the projections of old event classes. For both initial training and incremental-trainingg the projection vector (label embedding) dimensions are kept the same.
In CGML, the authors sample two graphs. Both of them have the same number of nodes, with an equal number of nodes in each class. We decouple the graph rogularisation and upper bound of the graph consis- tency Eqn. and Equ. of CGML using our Lemuna 3 and Lemma 5 respectively. This reduces number of samplings that we need for single epoch making the loss additive as we progress with the batching.
Incremental Block Wise Training
the proposed affinity and alignment losses regularizer to the triplet loss (Cerip) in the model. We train encoder model using the final loss Laffinity Calign + Cirip- This refines the features from the attention step Xatin, and we obtain the final features Xfinat- In our incremental setting, the encoder is trained sequentially on data blocks, updating its parameters after each block to retain previously learned information without retraining from scratch. We follow the 'latest message' training strategy of Cao let al. (202T) with the loss function defined above. After training, the resulting features can segregated using any clustering algorithm. This approach also allows for efficient inference, where a model trained on one block can be applied to w subsequent blocks.
For training,
Time Complexity
For block Ha with V nodes and E edges across all the layers, the time for constructing updating the heterogencous message graphs is O(E). The propagation step requires O(E) time. For a minibatch, the sampling step requires Yı 122 * * ni time, where n; is the number of neighbours sampled for a node in the GAT layer and I is the number of layers. The propagation step from the GAT module requires (Bd,, + Bedou)) time, where din dout; B, and B, are the input, output dimensions of GAT, node and odge size of the minibatch respectively. The loss functions and attention module require O(B²) and O (ond))) time. The practical running times are shown in the supplementary.
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Table 2: Event Detection performance for various traditional and supervised methods in terms of ARI.
Block
BERT
BILSTM
EventX
KPGNN
KPGNN:
FinEvanty
GLKD
Contextual
Proposed
Euglinh
F1
0.03
60
0.15
60
0.00
1 .06
b:02 .01
6.05
0/4
0.05
0/2
0.04 .00
0.04
1 0.022
0.97 1 0.01
B2
0.24 + .00
0.02
00
0.42 + .00
0.60 .02
0.65 = 05
0.88
01
0.09 .01
0.08
0.03
0.90 0.02
18g
0.11 - 00
0.08
00
0.04 1: .00
0.44 4: .03
0.50 = 01
0.52
00
0. 56
02
0.34 0.04
0.96 1 0.0a
18g
0.02 - 00
0.01
00
0.03 1 -00
0.22 4 .02
0.24 = 02
0.37
2 -01
0.22 2:22:11 .01
0.28 0.04
0.87 0.02
B6
0.02 - -00
0.00
00
0.00 + .00
0.32 .01
0.36
02
0.49
00
0.32
.08
0.48
0.00
0.91 + 0.011
B6
0.02 + 00
0.02
00
0.1 .00
0.46
.01
0.61
04
0.61
01
0.62
.01
0.64 0.04
0.95 0.01
B7
-0.02
+ .00
0.34
00
0.00
1: .00
0.06
001
0.06
02
0.08
01
0.07
.1
0.06 0.08
0.93 0.01
E8
00
0.02
00
0.09
.00
0.48
0.50
01
02
0.46
0-51 0.03
U.58 0.04
Eg
00
0.00
.00
0:28
0.30
03
81
0-41 0.02
0.01 0.02
R1D
R11
R12
B13
00
00
0.30
00
O.
0.00 0.02
00
0.02
00
0.03
0.22
01
35
0.29
0.04 0.02
B16
0.01
DO
0.03
00
0.01 + .00
0.10
+ .01
0.07
03
0.16
01
0.41
0.00
0.02
0.92 0.01
B1G
0.00
+ DO
0.03
00
0.04 + .00
0.44
.01
0.45
01
0.48
00
0.67
.22
0.94 0.02
0.00 0.01
B17
0.00
00
0.03
00
0.02 + .00
0.31
.02
0.81
02
0.32
02
0.36
.02
0.38 0.04
0.93 0.02
R18
0.01
80
0.03
00
0.03 1: .00
0:20
2 .03
0:22
01
0.35
01
0.38
.01
0.33 0.03
0.86 0.03
B19
0.01
80
0.02
DO
0.03 1. .00
0.26
.02
0:24
01
0-48
.01
0.35
.02
0.38
0.01
0.89 0.02
B20
0.02 = DO
0.02
DO
0.04
I .00
D.ST
.02
0.34
0-40
DO
0.34
.02
0.37 4: 0.04
0.83 1 0.02
0.03
D0
0.03
00
0.01 A .00
0.11 .01
0:30
002
0.22
02
0.37
.0
0.14 4 0.02
0.91 0.01
Freach
751
0:01 1 0.00
0-03
0.00
0.00 1. .00
0:26 2 0-02
0.28 I 0.02
0:33
1 0:01
0.29 1 0
0-30 0.0.
0.92 1:921:00 0.01
0.04
0.00
0.01
0.01
0.01
+ .00
0.81
0.01
0.30
0.03
0.34
0.04
0,38
0 08
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0.02
0.00
0.01
0.01
0.01
+ .00
0.01 0.35
0.34
0.01
0.37 + 0.01
0.40
+
02
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0.92 1: 0.02
B4
0.03
0.00
0.02
0.00
0.01
.00
0.02
0.02
0.25 0.02
03
0-38 0.03
0.90 0.01
E6
0.88
0.00
0.02
0.01
0.03
.00
0.02 0.37
0.30 0.01
0.34 0.01
0.01
0.40 0.03
0.50 0.01
B6
8.00 0.83
0.01 0.08
0.01
0.02
0.20 0.03
0.18 0.00
03
0.47 0.02
0.93 0.02
11
R8
B6
0.01 0.01
0.02 0.16
0.18
0.001
0.78 0.01
B10
0.07
0.00
0.06 0.00
0.02
0.01
0.19 0.01
0.27 0.01
0.40 + 0.04
0.39 0.308
0.57 0.02
0.06
0.02
0.02
0.02
0.18 0.00
0.25
0.30
0.90 0.01
B12
0.06
0.00
0.05 0.00
0.02 + .06
0.20
+ 0.03
0.23 0.04
0.28 1 0.02
0.57
+ 0.02
0.44+0.02 0.0.
0.00 0.02
B18
0.02 + 0.00
0.01
0.00
0.01
+ .06
0.17
+ 0.01
0.22 + 0,01
0.20 0.03
0.37 + 0.03
0.04 0.0.8
0.05 + 0.03
R14
0.02
2:0 0.00
0.02 2 0.00
0.03
1 .00
0.16
0-02
0.33
2:332000 0.02
0.31 2 0.04
0.54 0.02
0-62
1 0.02
0.94 0.02
B15
0.01
0.00
0.04
0.01
0.02
1 .00
0.24
0.04
0.28
2 6.03
0.36 2 0.02
0.54 1 0.05
0.48 3 0.02
0.89 0.02
Experiments and Results
Data Sets: We use two publicly available large-scale datasets Event2012 (English) (McMinn al. 2013) and Event2018 (French) (Mazoyer et al. 22200), designed to evaluate social event detection. Event2012
comprises 68, 841 manually labelled tweets spanning 503 event classes over 29 days. Event2018 with 64, 516 labelled tweets across 257 event classes, covering 23 days, is used for cross-lingual experiments. The evolving social messages are split into blocks by date, according to the paper (Peng et al. 2023
Baselines and Evaluation Metrics: We compare our model with BERT Devlin et al. 2019 BiI.-
STM Graves & Schmidhuber 2005). EventX (Liu et al.] 2020), KPGNN (aoo et al.) 2021), Fin Event Peng ct al. 2023), CLKD(Renn et al., (024) and Contextual (Liao al. 2023). For complete details on the exper- imental settings, code and dataset, the reader can refer to the supplementary material. In alignment with prior studies (Cao et al. 2021), we utilise established metrics such as Adjusted Mutual Information (AMI),
Adjusted Rand Index (ARI), and Normalised Mutual Information (NMI).
0.8
0.5
3...
0.2
000
0.0
5
BERT
B1.TM
Pveettx
Kromn
FeEwert
Propored
Figure 4: Dot plot comparing proposod model with some supervised models in terms of NMI and AMI.
Results
Table 2 and Figure 4 show our results compared to supervised baselines in an online setting. We run our model 5 times with random seeds and report the average results. It is evident that our model consistently outperforms all supervised basclines across all metrics in both datascts. We see an average increase of 21.53% (NMI), 25.32% (AMI), and 146.25% (ARI) from the second-highest bascline for the English dataset. Similarly, an average increase of 26.57% (NMT), 29.15% (AMT), and 101.13% (ART) is observed from the
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NMI
55
1.0
0.4
1.6
0.4
HyperSED
Figure 5: Dot plot comparing proposed supervised model with unsupervised models.
NMI
AMI
ARI
0.4
Attention
Only Triplet
Attention Affinity Alignment
Alfinity + Alignment
Figure 6: Dot plot showing the contribution of different components of our model for the English dataastt.
NMII
AMI
ARI
Sco0e
0.5
A
Attention
Only Tiplet
Attention Affinity Alignment
Alfinity + Alignment
Figure 7: Dot plot showing the contribution of different components of our model for the French dataset.
second-highest baseline for the French dataset. The numerical results with standard deviation for NMT and AMI metrics provided in the supplementary file. To validate observations, performed statistical significance analysis using the Wilcoxon signed-rank test on the paired performance differences across all data blocks. The null hypothesis (Ho) indicates that the median of the performance difference between our proposed method and the second-best. supervised baseline method is zero. The alternative hypothesis (H₂) is that this median is greater than zero, indicating our method's superiority. The test results, focusing on the combined NMI, AMI, and ARI metrics, confirm that our method's improved performance is statistically significant across both datasets. For the English dataset, we have p 0.00001 and for the French dataset, get p 0.00003. In both cases, the null hypothesis is decisively rejected in favour of the alternative, indicating improved performance with our proposed method.
Ablation Study
Comparison with Unsupervised Methods: We compare the proposed method with the unsupervised methods HISEvent Cao al. (2024) and HyperSED Yu et al. (2025) and the results are shown Figure We can observe from the Figure 5 that for the English dataset, our method outperforms HISEvent and HyperSED across majority of blocks (ahead in 14, 15 and 19 blocks out of 21 in terms of NMT, AMT and ART
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Published in Transactions on Machine Learning Research (02/2026)
respectively), with average improvements of 3.88%, 4.09%, and 15.27% for NMI, AMI, and ARI, respectively. Similarly, for the French dataset, our method leads in most blocks, e.g., 11 in NMT and AMI, and all blocks in ARI, with average gains of 6.06% (NMI), 6.44% (AMI), and 26.47% (ARI). We perform a statistically significant test to validate the observations using the Wilcoxon signed-rank test. The mull hypothesis (Hu) posits that the median of the performance difference between our method and a competitor zero, while the alternative hypothesis (H1) is that this median is greater than zero. The test results, focusing on the combined metric, confirm that our method's superiority is statistically significant across both datasets. For the English dataset, the proposed method shows highly significant improvement over both HISEvent (p < 0.00001) and HyperSED (p ~ 0.0003). Similarly, for the French dataset, the method demonstrates a significant improvement over HISEvent (p ~ 0.00006) and HyperSED (p ~ 0.00003). Thus, in all cases, the null hypothesis is rejected in favour of the alternative, supporting our claim of superior performance. A slight decrease in our NMI and AMI scores is sometimes observed due to the operation of our model. It learns to group events by aligning them near each other in a shared label space, This approach can become challenge when handling events that are separate but closely related, especially those that occur one after the other. Because of the connection between these consecutive events, they are mapped to adjacent regions in the label space. This closeness can cause the model to incorrectly merge the distinct events into a single cluster. This has a stronger negative impact on the NMI and AMI scores than on other metrics. However, this appears to be a minor trade-off, as our method's superior performance in most cases demonstrates its overall effectiveness.
Impact of Individual Components: To isolate the impact of our loss functions' components, we per- formed an ablation study comparing our final loss, ffinal, to the standard triplct loss (Ctrip) from Cao et al. (202T). The results for the English datasel are shown in Figure 6 Our attention module alone enhances the baseline triplet loss significantly, with average improvements of 21.80% (NMI), 27.14% (AMI), and 254.29% (ARI). In some blocks, however, its performance decreases, which we attribute to noise from heterogeneous interactions. Our proposed loss enhances the triplet loss by 27. 27.20% in NMI, 30.63% in AMI, and 132.02% in ARI. By combining these elements, our complete loss function, fifinat, achieves the most substantial im- provement, surpassing the baseline triplet loss by an average of 59.84% in NMI, 70.69% in AMI, and 470.95% in ARI. For the French dataset (Figure 7 the attention module enhances the baseline triplet loss by 73.44% (NMI), 79.30% (AMI), and 261.21% (ARI). Only our proposed loss enhances the triplet loss by 83.15% in NMI, 90.22% in AMI, and 266.57% ARI. Combining all the elements (final) we obtain an improvement of 100.08% (NMI), 108.55% (AMI), and 298.22% (ARI). Sensitivity analysis for hyperparameters are reported in Figures 11 and 12 in the supplementary.
Generalizability of the Proposed Loss: In order assess the broader applicability of proposed Affin- ity and Alignment losses beyond the domain of social event detection, we evaluated their generalization capabilities against 25 different metric learning losses on four widely-used benchmark datasets: Cora (cita- tion network), Amazon Computers and Amazon Photo (co-purchase networks), and Flickr (social network). The experiment learns discriminative representations using each loss in a standardized supervised train- ing framework. We evaluated the quality of the learned embedding space by applying K-Means clustering and quantifying performance using NMI, AMI, and ARI. As detailed in Table 3 our method consistently demonstrates robust performance against a comprehensive suite of state-of-the-an metric learning baselines. Notably, it achieves significant gains on the Computers and Photo datasets, with average improvements of 6.42% and 7.32% respectively across all metrics, and secures 10% increase in ARI for both. On the Flickr and Cora datasels, our approach remains highly competitive, matching or surpassing the majority of estab- lished baselines. These results empirically validate that the proposed Affinity and Alignment losses function effectively enforce feature consistency and separation on generalized tasks across diverse data domains.
We observed that for some of the cases, closcly related events are merged into onc. In order to understand the rationale, we analyzed one of the low NMI block, 'Block 15', of the English dataset. A visual comparison of the TSNE embeddings of our features with the ground truth and the predicted event classes are shown in Figure g We specifically focus on the predicted cluster (Cluster 11) (markod with the black cireles in the right Figure) exhibiting the highest mixture of ground truth events. Our analysis reveals that there are noisy (spurious missing) connections wetween these tweets based on the common attributes
Failure Case Analysis:
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Table Generalization performance to standard metric learning benchmarks.
Cora
Computers
Flickr
Photo
Loss
NMI
AMI
ART
NMI
AMI
ARI
NMI
AMI
ARI
NM1
AMI
ARI
TripltMaaghh Balntas et al.
2016
0.55
0.54
0.51
0.62
0.62
0.62
0.01
0.01
0.00
0.78
0.77
0.70
Contrastive Muserave
0.41
0.41
0.16
0.61
0.61
0.51
0.08
0.08
0.08
0.75
0.74
0.64
MultiSmmllarity Wang
0.50
0.43
0.02
0.00
0.02
0.01
Circle (Sn ct al 2rzu
0.36
0.55
0.53
0.51
0.51
0.27
0.01
0.01
0.00
0.66
0.65
0.49
NTXent Chin
220
0.37
0.56
0.57
0.74
0.74
SupCon Khosla ot al. 2020
0.37
0.56
0.55
0.68
0.68
0.67
0.01
0.01
0.02
0.78
0.76
0.68
LiftedSeructure [Song
2016
0.44
0.41
0.18
0.44
0.43
0.27
0.01
0.01
0.00
0.69
0.69
0.57
NCA Golddberec
0,30
0.56
0.00
0.79
0.74
Margin Wi cf al. 2011
0.02
0.00
0.00
0.03
0.02
0.0L
0.01
0.01
0.00
0.03
0,03
0.01
SNR (Yua
0.62
FastAP Faragg 2019
0.59
0.59
0.60
0.03
0.02
0.02
0.07
0.07
0.07
0.77
0.77
0.76
GenLiftedS Hermans
2017
0.48
0.48
0.27
0.16
0.45
0.02
0.02
0.03
0.73
0.73
0.65
Iisstorram (Ustinow & Lemp
0.37
0.09
0.69
NPairs (Cogewell es at.
2016
2016
0.50
0.49
0.37
0.03
0.02
0.0L
0.01
0.01
0.00
0.03
0.02
0.01
RaukedLsst Wadt
SmoothAP B3ownn et Bl. 2020
0.05
0.04
0.02
0.12
0.12
0.06
0.04
0.04
0.06
0.30
0,90
0.17
ArcFace Deng
0.51
0.50
0.40
0.
0.41
0.10
0.10
0.06
0.28
0.27
0.16
Wang M1.
0.59
0.60
LeMarginSottaass Lib et
2016
0.00
0.00
0.00
0.31
0.30
0.10
0.00
0.00
0.00
0.00
0.00
0.00
ProcyAuchor Kiiu ei
ProxyNCA Movshovisz Altaas et al. 2017
0.52
0.51
0.38
0.62
0.62
0.49
0.11
0.11
0.06
0.76
0.76
0.71
Sul/Triple (Qan 2019
0.58
0.57
0.53
0.61
0.64
0.56
0.10
0.10
0.08
0.76
0.76
0.66
P2SGrad 2ZEAng
0.53
0.63
PNP [L el RL. 2022
0.58
0.57
0.57
0.62
0.62
0.52
0.11
0.11
0.07
0.71
0.74
0.63
0.33
Ours
0.38
0.56
0.58
0.71
0.71
0.74
0.11
0.11
0.09
0.83
0.83
0.85
Ground Truhh
Predicted Event Classes (Hightghting
-20
-20
-40
-40
60
Figure 8: TSNE plots for Ground Truth Events and Predicted Events for the embedding of block B15-
(entities, words and mentions) provided by the dataset. This can be seen the Table 12 (supplementary) and the Figure 9b) where we show the tweets of Cluster 11 and visualize the different relations between them respectively. Along with this, also find that concatenating the timestamp encodings of tweet with its text encodings increases the feature similarity between tweets of different events that arrive within similar time frame. We highlight this in Figure Da for the tweets in Cluster 11 which appear in the time window of a day.
Conclusion
In this paper, we addressed the fundamental limitations of triplet-loss-bssed frameworks for social event detection. Through two novel regularising losses, namely, Affinity Loss and Alignment Loss, we resolve these challenges. Our approach ensures consistent feature separation by increasing intra-event affinity while aligning features a common label space across minibatches. We have theorctically demonstrated that our joint loss optimisation is no less discriminative than CGML, while operating lincarly over minibatches, The proposed attention module iliieses heterogeneous attributes effectively.
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2364
2138
ESPT
2475
5428
Dend5y
2435
2492
2323
20L5
2515
2348
-0.25
0.00
0.25
5.50
0.75
1.00
1:23
1.50
Coshne Distance
(a) Distribution of pairwise cosine distances
(b) Homogeneous layer graph for Cluster 11 Figure 9: Analysis of Cluster 11 in Block B15-
The contributions of this work may be extended beyond event detection. For example, the proposed Affinity and Alignment losses can offer a robust solution for any deep metric learning lask that relies on minibatch training and suffers from feature inconsistency.
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Reviews:
[{"review_id": "R3KH91FLVk", "reviewer": "Reviewer_SV4W", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces a new deep metric learning framework for incremental social event detection, addressing the fundamental weaknesses of triplet-loss-based models—namely inconsistent intra-/inter-event distances and the inability to align same-event samples across mini-batches. We propose two novel regularizers, Affinity Loss and Alignment Loss, which jointly enforce discriminative structure by increasing intra-event affinity and aligning features to a shared label space, theoretically matching the discriminative guarantees of CGML while avoiding its expensive pairwise sampling. Alongside a cross-layer multi-head attention module that effectively integrates heterogeneous relations without extra optimization, the model achieves substantial gains on Event2012 and Event2018, improving over supervised SOTA baselines by 26.59% (NMI), 30.49% (AMI), and 142.38% (ARI) on average, and outperforming unsupervised methods as well. The framework scales linearly with mini-batches, supports efficient incremental training, and offers a broadly applicable solution for metric learning tasks suffering from feature inconsistency.", "claims_and_evidence_yn": "Yes", "claims_explanation": "1. The paper provides formal proofs and lemmas demonstrating that the proposed Affinity and Alignment losses achieve discriminative properties comparable to CGML, but with far lower computational cost. This theoretical grounding directly supports the claim that the new losses produce consistent latent-space structure.\n2. Across two public large-scale datasets (Event2012 and Event2018), the method consistently outperforms all supervised and unsupervised baselines by large margins—e.g., NMI/AMI/ARI improvements of 26.59%, 30.49%, and 142.38%, respectively—providing clear, quantitative evidence that the proposed approach is effective in practice.\n3. The paper goes beyond raw scores by including Wilcoxon signed-rank tests showing the improvements are statistically significant (p < 0.0001 in most cases). Detailed ablation studies further isolate the contributions of each component (attention module, Affinity Loss, Alignment Loss), confirming that every claim is causally supported by controlled experiments.", "audience_yn": "Yes", "audience_explanation": "1. The paper proposes new loss functions (Affinity & Alignment) with solid theoretical backing, directly addressing well-known weaknesses of triplet loss. This contributes to the broader research agenda on robust metric learning, discriminative representations, and training stability—topics central to TMLR’s audience.\n2. Incremental social event detection is an active and socially important problem involving streaming data, heterogeneous graphs, and online learning. Readers interested in real-world applications of representation learning—especially in social media analysis, crisis detection, and temporal graph mining—will find the approach directly useful.\n3. The proposed Affinity/Alignment losses and cross-layer attention module are generic and can be plugged into many other graph-based or minibatch-based learning systems. TMLR readers who work on GNNs, heterogeneous data, or scalable learning algorithms may find these components directly applicable to their own domains.", "requested_changes": "1. The paper claims broad applicability of Affinity/Alignment losses beyond event detection, but the experiments are limited to two datasets from similar domains. Clarify the generality claims and, if possible, provide a small supplemental experiment or discussion showing applicability to other metric-learning or graph-learning settings.\n2. Although classical baselines are included, comparisons with more recent deep metric learning methods are not sufficiently explored. Add justification for baseline selection or include one additional modern metric-learning baseline to strengthen the empirical comparison.\n3. The ablation only evaluates the presence or absence of modules, but does not analyze sensitivity to key hyperparameters. Include a short sensitivity analysis or discussion explaining hyperparameter stability.\n4. The paper briefly mentions performance drops on closely related consecutive events, but the explanation is superficial. Provide clearer examples or a deeper explanation of when alignment may cause cluster merging, and propose possible remedies.\n5. The role of each heterogeneous relation (words, entities, users) is not quantified, making it hard to assess the value of the cross-layer attention. Add a relation-importance analysis or a brief visualization demonstrating how the attention weights differ across relations.\n6. The description of block-wise incremental learning and the “latest message” strategy lacks low-level details. Include a more explicit description or pseudo-code of the incremental training pipeline to improve reproducibility.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "sLk06qRqe6", "reviewer": "Reviewer_xQJE", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "To address the limitations of existing methods in event detection, particularly those based on triplet loss, the authors propose a hierarchical modeling approach and two novel loss functions. Experimental results demonstrate a significant performance improvement.\n\n**Strengths**  \n1. The authors' motivation and approach are well-motivated, straightforward, and clear, pinpointing the key limitation of the triplet loss.  \n2. The method proposed by the authors achieves highly competitive results, with the visualization in Fig. 10 being particularly remarkable.\n\n**Weakness**\n1. Although the proposed two key losses demonstrate superior performance in the experimental results (Fig. 7), they do not appear to be specific to the problem of Event Detection. In other words, these loss strategies may be adapted to arbitrary classification tasks. They do not show what makes Event Detection different or difficult.  \n2. I am uncertain about how the authors set up the label information, i.e., $f_y(y_i)$ . To my knowledge, in scenarios like continual learning where new categories (which would correspond to new event types in this work) are introduced, it is crucial to explain how the model maintains consistent dimensions for label representation or how these new event types are defined.  Lemmas 6 and 7 suffer from similar issues.\n3. Some of the lemmas appear unduly cumbersome. For example, the detailed exposition of foundational concepts like the triangle inequality in Lemma 4 seems dispensable in the main text and could be moved to the Appendix. The authors should focus on providing rigorous reasons for why these losses satisfies the conditions of the Discriminative Graph.  \n4. Other typos. In Eq. 1, $\\sum_{\\forall s_n \\in V}$ appears to be incorrect. It should probably be over the neighbour nodes, i.e., $\\forall s_n \\in N_{s_n}$？ \n\nIn summary, my primary concerns are the oversimplified loss configuration and some lemmas are too redundant.   \n\n\n**Update** \nThe authors have addressed the majority of my concerns.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The method proposed by the authors is effective, as clearly demonstrated by the significant results. Furthermore, ablation studies and visualizations also validate the efficacy of the proposed modules.", "audience_yn": "Yes", "audience_explanation": "The hierarchical processing proposed by the authors is relatively novel. However, the two proposed loss functions are somewhat conventional, as they are not specifically tailored for the event detection scenario.", "requested_changes": "Please refer to the weaknesses.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "vdaIxD1EqP", "reviewer": "Reviewer_dgQK", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper takes social event detection beyond triplet loss by enforcing both local (within-batch) and global (across-batch) consistency via new regularized losses and scalable attention, achieving state-of-the-art results on real-world datasets.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The submission provides extensive analysis comparing the proposed approach to supervised and unsupervised baselines, featuring both quantitative and qualitative evaluations across multiple metrics (NMI, AMI, ARI) on two datasets (English and French). Statistical significance is established using the Wilcoxon signed-rank test, confirming that gains over the best existing methods are highly significant. The ablation study further demonstrates that each model component—the new attention module, affinity loss, and alignment loss—contributes distinct, measurable improvements. Some minor limitations are acknowledged: the method may occasionally merge closely related consecutive events, impacting certain metrics (NMI/AMI) but not undermining the overall advantage. Thus, the experimental design, theoretical support, and comprehensive empirical results together provide clear, consistent, and convincing evidence for the claimed contributions and superiority of this work.", "audience_yn": "Yes", "audience_explanation": "Yes, at least some individuals in TMLR's audience would be interested in the findings of this paper, as it addresses key limitations of deep metric learning—such as latent space consistency and scalable attention mechanisms—which are central topics in machine learning research. While the application is focused on social event detection, the proposed affinity and alignment loss functions and the cross-layer attention mechanism generalize to other areas of deep metric learning, potentially offering methodological insights relevant for broader ML problems. Thus, its findings hold value for researchers studying representation learning, graph neural networks, and scalable learning algorithms in practical settings.", "requested_changes": "Critical Adjustments (necessary for acceptance):\n\n- Clarity and Limitations: Explicitly discuss and quantify cases where closely related consecutive events are merged by the model, affecting NMI/AMI scores. Suggest mitigation strategies or clarify when this behavior is or is not desirable.\n\n- Generalizability: Strengthen discussion about how the proposed affinity and alignment loss functions can be applied to other domains beyond social event detection, especially for TMLR's machine learning-focused audience.\n\nNon-Critical Adjustments (would strengthen the work):\n\n- Implementation Details: Increase transparency regarding data splits, hyperparameters, and model settings to foster reproducibility.\n\n- Related works: Zhang Y, Cao D, Liu Y. Counterfactual neural temporal point process for estimating causal influence of misinformation on social media[J]. Advances in Neural Information Processing Systems, 2022, 35: 10643-10655.\n\n- Supplementary Materials: Add sample code or pseudo-code for the proposed losses and attention mechanisms.\n\n- Visualization: Include more visual examples showcasing model behaviours (e.g., alignment in embedding space, event clusters).", "broader_impact_concerns": "NA", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)